library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotation
library(mixOmics) #multilevel PCA and sPLS-DA
library(lipidr)
library(patchwork)
library(tidyverse) # for everything## [1] 2450
## # A tibble: 12 × 89
## mz_rt dupe_count row_id x5109_b1_beta_c18pos…¹ x5101_b1_control_c18…²
## <chr> <int> <dbl> <dbl> <dbl>
## 1 368.2792_0.6… 2 1042 10114. 24509.
## 2 368.2792_0.6… 2 1057 10114. 24509.
## 3 717.5903_6.8… 2 4952 31805. 49139.
## 4 717.5903_6.8… 2 4956 31805. 49139.
## 5 720.5531_6.8… 2 5040 20646 23011.
## 6 720.5531_6.8… 2 5070 20646 23011.
## 7 812.6663_7.96 2 7214 20484. 24697.
## 8 812.6663_7.96 2 7301 20104. 24697.
## 9 814.5717_7.3… 2 6253 10382. 13812.
## 10 814.5717_7.3… 2 6257 10382. 13812.
## 11 931.5277_8.5… 2 8080 18478. 15492.
## 12 931.5277_8.5… 2 8092 18478. 15492.
## # ℹ abbreviated names: ¹x5109_b1_beta_c18pos_65, ²x5101_b1_control_c18pos_82
## # ℹ 84 more variables: x5109_b3_beta_c18pos_81 <dbl>,
## # x5103_b3_lyc_c18pos_63 <dbl>, x5108_b1_control_c18pos_71 <dbl>,
## # x5113_b1_beta_c18pos_53 <dbl>, x5112_b3_beta_c18pos_56 <dbl>,
## # x5105_b1_lyc_c18pos_76 <dbl>, x5110_b3_lyc_c18pos_84 <dbl>,
## # x5110_b1_lyc_c18pos_59 <dbl>, x5101_b3_control_c18pos_66 <dbl>,
## # x5113_b3_beta_c18pos_47 <dbl>, x5103_b1_lyc_c18pos_35 <dbl>, …
# remove dupes
omicsdata <- omicsdata %>%
distinct(mz_rt, .keep_all = TRUE)
# check again for dupes
omicsdata %>% get_dupes(mz_rt)## # A tibble: 0 × 89
## # ℹ 89 variables: mz_rt <chr>, dupe_count <int>, row_id <dbl>,
## # x5109_b1_beta_c18pos_65 <dbl>, x5101_b1_control_c18pos_82 <dbl>,
## # x5109_b3_beta_c18pos_81 <dbl>, x5103_b3_lyc_c18pos_63 <dbl>,
## # x5108_b1_control_c18pos_71 <dbl>, x5113_b1_beta_c18pos_53 <dbl>,
## # x5112_b3_beta_c18pos_56 <dbl>, x5105_b1_lyc_c18pos_76 <dbl>,
## # x5110_b3_lyc_c18pos_84 <dbl>, x5110_b1_lyc_c18pos_59 <dbl>,
## # x5101_b3_control_c18pos_66 <dbl>, x5113_b3_beta_c18pos_47 <dbl>, …
## [1] 2444
Sometimes a weird logical column (lgl) comes up in my data. Let’s check if it’s there
## [1] "mz_rt" "row_id"
## [3] "x5109_b1_beta_c18pos_65" "x5101_b1_control_c18pos_82"
## [5] "x5109_b3_beta_c18pos_81" "x5103_b3_lyc_c18pos_63"
## [7] "x5108_b1_control_c18pos_71" "x5113_b1_beta_c18pos_53"
## [9] "x5112_b3_beta_c18pos_56" "x5105_b1_lyc_c18pos_76"
## [11] "x5110_b3_lyc_c18pos_84" "x5110_b1_lyc_c18pos_59"
## [13] "x5101_b3_control_c18pos_66" "x5113_b3_beta_c18pos_47"
## [15] "x5103_b1_lyc_c18pos_35" "x5104_b3_lyc_c18pos_32"
## [17] "x5112_b1_beta_c18pos_34" "x5107_b3_beta_c18pos_28"
## [19] "x5107_b1_beta_c18pos_29" "x5102_b3_control_c18pos_33"
## [21] "x5111_b3_control_c18pos_39" "x5105_b3_lyc_c18pos_21"
## [23] "x5108_b3_control_c18pos_22" "x5102_b1_control_c18pos_5"
## [25] "x5111_b1_control_c18pos_4" "x5104_b1_lyc_c18pos_10"
## [27] "x5115_b3_lyc_c18pos_70" "x5115_b1_lyc_c18pos_36"
## [29] "x5125_b1_control_c18pos_80" "x5118_b1_lyc_c18pos_57"
## [31] "x5121_b1_beta_c18pos_69" "x5119_b1_control_c18pos_74"
## [33] "x5122_b3_lyc_c18pos_75" "x5117_b1_lyc_c18pos_46"
## [35] "x5114_b3_beta_c18pos_23" "x5124_b3_control_c18pos_68"
## [37] "x5125_b3_control_c18pos_51" "x5114_b1_beta_c18pos_26"
## [39] "x5120_b1_beta_c18pos_72" "x5119_b3_control_c18pos_58"
## [41] "x5121_b3_beta_c18pos_48" "x5116_b3_control_c18pos_20"
## [43] "x5126_b1_beta_c18pos_62" "x5123_b3_control_c18pos_40"
## [45] "x5116_b1_control_c18pos_6" "x5124_b1_control_c18pos_45"
## [47] "x5117_b3_lyc_c18pos_2" "x5122_b1_lyc_c18pos_14"
## [49] "x5118_b3_lyc_c18pos_9" "x5123_b1_control_c18pos_11"
## [51] "x5120_b3_beta_c18pos_54" "x5127_b3_control_c18pos_83"
## [53] "x5129_b1_lyc_c18pos_78" "x5128_b3_lyc_c18pos_60"
## [55] "x5134_b3_beta_c18pos_64" "x5136_b3_beta_c18pos_77"
## [57] "x5127_b1_control_c18pos_30" "x5132_b3_beta_c18pos_52"
## [59] "x5130_b3_lyc_c18pos_50" "x5131_b1_beta_c18pos_38"
## [61] "x5126_b3_beta_c18pos_24" "x5129_b3_lyc_c18pos_16"
## [63] "x5134_b1_beta_c18pos_42" "x5132_b1_beta_c18pos_41"
## [65] "x5133_b1_control_c18pos_27" "x5136_b1_beta_c18pos_44"
## [67] "x5130_b1_lyc_c18pos_17" "x5128_b1_lyc_c18pos_8"
## [69] "x5135_b3_lyc_c18pos_18" "x5133_b3_control_c18pos_15"
## [71] "x5131_b3_beta_c18pos_3" "x5135_b1_lyc_c18pos_12"
## [73] "qc3_c18pos_13" "qc2_c18pos_7"
## [75] "qc11_c18pos_61" "qc14_c18pos_79"
## [77] "qc9_c18pos_49" "qc10_c18pos_55"
## [79] "qc6_c18pos_31" "qc8_c18pos_43"
## [81] "qc5_c18pos_25" "qc15_c18pos_85"
## [83] "qc13_c18pos_73" "qc7_c18pos_37"
## [85] "qc12_c18pos_67" "qc4_c18pos_19"
## [87] "qc1_c18pos_1" "x88"
# remove lgl column
omicsdata <- omicsdata %>%
dplyr::select(!where(is.logical))
colnames(omicsdata)## [1] "mz_rt" "row_id"
## [3] "x5109_b1_beta_c18pos_65" "x5101_b1_control_c18pos_82"
## [5] "x5109_b3_beta_c18pos_81" "x5103_b3_lyc_c18pos_63"
## [7] "x5108_b1_control_c18pos_71" "x5113_b1_beta_c18pos_53"
## [9] "x5112_b3_beta_c18pos_56" "x5105_b1_lyc_c18pos_76"
## [11] "x5110_b3_lyc_c18pos_84" "x5110_b1_lyc_c18pos_59"
## [13] "x5101_b3_control_c18pos_66" "x5113_b3_beta_c18pos_47"
## [15] "x5103_b1_lyc_c18pos_35" "x5104_b3_lyc_c18pos_32"
## [17] "x5112_b1_beta_c18pos_34" "x5107_b3_beta_c18pos_28"
## [19] "x5107_b1_beta_c18pos_29" "x5102_b3_control_c18pos_33"
## [21] "x5111_b3_control_c18pos_39" "x5105_b3_lyc_c18pos_21"
## [23] "x5108_b3_control_c18pos_22" "x5102_b1_control_c18pos_5"
## [25] "x5111_b1_control_c18pos_4" "x5104_b1_lyc_c18pos_10"
## [27] "x5115_b3_lyc_c18pos_70" "x5115_b1_lyc_c18pos_36"
## [29] "x5125_b1_control_c18pos_80" "x5118_b1_lyc_c18pos_57"
## [31] "x5121_b1_beta_c18pos_69" "x5119_b1_control_c18pos_74"
## [33] "x5122_b3_lyc_c18pos_75" "x5117_b1_lyc_c18pos_46"
## [35] "x5114_b3_beta_c18pos_23" "x5124_b3_control_c18pos_68"
## [37] "x5125_b3_control_c18pos_51" "x5114_b1_beta_c18pos_26"
## [39] "x5120_b1_beta_c18pos_72" "x5119_b3_control_c18pos_58"
## [41] "x5121_b3_beta_c18pos_48" "x5116_b3_control_c18pos_20"
## [43] "x5126_b1_beta_c18pos_62" "x5123_b3_control_c18pos_40"
## [45] "x5116_b1_control_c18pos_6" "x5124_b1_control_c18pos_45"
## [47] "x5117_b3_lyc_c18pos_2" "x5122_b1_lyc_c18pos_14"
## [49] "x5118_b3_lyc_c18pos_9" "x5123_b1_control_c18pos_11"
## [51] "x5120_b3_beta_c18pos_54" "x5127_b3_control_c18pos_83"
## [53] "x5129_b1_lyc_c18pos_78" "x5128_b3_lyc_c18pos_60"
## [55] "x5134_b3_beta_c18pos_64" "x5136_b3_beta_c18pos_77"
## [57] "x5127_b1_control_c18pos_30" "x5132_b3_beta_c18pos_52"
## [59] "x5130_b3_lyc_c18pos_50" "x5131_b1_beta_c18pos_38"
## [61] "x5126_b3_beta_c18pos_24" "x5129_b3_lyc_c18pos_16"
## [63] "x5134_b1_beta_c18pos_42" "x5132_b1_beta_c18pos_41"
## [65] "x5133_b1_control_c18pos_27" "x5136_b1_beta_c18pos_44"
## [67] "x5130_b1_lyc_c18pos_17" "x5128_b1_lyc_c18pos_8"
## [69] "x5135_b3_lyc_c18pos_18" "x5133_b3_control_c18pos_15"
## [71] "x5131_b3_beta_c18pos_3" "x5135_b1_lyc_c18pos_12"
## [73] "qc3_c18pos_13" "qc2_c18pos_7"
## [75] "qc11_c18pos_61" "qc14_c18pos_79"
## [77] "qc9_c18pos_49" "qc10_c18pos_55"
## [79] "qc6_c18pos_31" "qc8_c18pos_43"
## [81] "qc5_c18pos_25" "qc15_c18pos_85"
## [83] "qc13_c18pos_73" "qc7_c18pos_37"
## [85] "qc12_c18pos_67" "qc4_c18pos_19"
## [87] "qc1_c18pos_1"
# create long df for omics df
omicsdata_tidy <- omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample",
values_to = "peak_height") %>%
mutate(sample2 = sample) %>%
# add a new column with just subject
dplyr::rename("subject" = sample2) %>%
# remove the suffix from subject names
mutate_at("subject", str_sub, start=2, end=5)## tibble [207,740 × 18] (S3: tbl_df/tbl/data.frame)
## $ subject : chr [1:207740] "5109" "5101" "5109" "5103" ...
## $ sample : chr [1:207740] "x5109_b1_beta_c18pos_65" "x5101_b1_control_c18pos_82" "x5109_b3_beta_c18pos_81" "x5103_b3_lyc_c18pos_63" ...
## $ treatment : chr [1:207740] "beta" "control" "beta" "red" ...
## $ tomato_or_control: chr [1:207740] "tomato" "control" "tomato" "tomato" ...
## $ mz : num [1:207740] 189 189 189 189 189 ...
## $ rt : num [1:207740] 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 ...
## $ row_id : num [1:207740] 204 204 204 204 204 204 204 204 204 204 ...
## $ peak_height : num [1:207740] 5394 3299 6079 4923 3050 ...
## $ sex : chr [1:207740] "M" "F" "M" "M" ...
## $ bmi : num [1:207740] 26.1 29.4 26.1 29.7 22.7 ...
## $ age : num [1:207740] 65 55 65 50 52 34 60 61 58 58 ...
## $ tot_chol : num [1:207740] 127 235 127 172 177 141 235 189 176 176 ...
## $ ldl_chol : num [1:207740] 69.4 150.4 69.4 111.4 120 ...
## $ hdl_chol : num [1:207740] 40 71 40 53 44 44 52 45 66 66 ...
## $ triglycerides : num [1:207740] 88 68 88 38 65 55 169 92 47 47 ...
## $ glucose : num [1:207740] 87 90 87 92 91 99 92 103 92 92 ...
## $ SBP : num [1:207740] 147 156 147 106 139 104 107 131 109 109 ...
## $ DBP : num [1:207740] 76 90 76 71 81 71 70 78 70 70 ...
# replace NA's in certain columns with QC
meta_omics_sep$subject <- str_replace_all(meta_omics_sep$subject, "c", "qc")
meta_omics_sep$sample <- meta_omics_sep$sample %>%
replace_na("QC")
meta_omics_sep$treatment <- meta_omics_sep$treatment %>%
replace_na("QC")
meta_omics_sep$tomato_or_control <- meta_omics_sep$tomato_or_control %>%
replace_na("QC")# plot
(plot_mzvsrt <- meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z",
title = "mz across RT for all features"))# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
dplyr::select(-contains("qc"))
#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))
hist(NAbyRow_noQC,
breaks = 70, # because there are 70 samples
xlab = "Number of missing values",
ylab = "Number of metabolites",
main = "How many missing values are there?")Are there any missing values in QCs? There shouldn’t be after data preprocessing/filtering
omicsdata_QC <- omicsdata %>%
dplyr::select(starts_with("qc"))
NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no## [1] 0
# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
group_by(mz_rt) %>%
dplyr::count(is.na(peak_height)) %>%
filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)| mz_rt | is.na(peak_height) | n |
|---|---|---|
| 1050.6573_2.737 | TRUE | 1 |
| 1051.1585_2.738 | TRUE | 1 |
| 1052.864_11.17 | TRUE | 2 |
| 1064.8634_11.211 | TRUE | 21 |
| 1077.6264_2.732 | TRUE | 2 |
| 1192.8093_6.322 | TRUE | 1 |
| 1213.4964_3.94 | TRUE | 3 |
| 1260.0333_9.997 | TRUE | 15 |
| 1272.1559_12.098 | TRUE | 1 |
| 1312.1372_10.911 | TRUE | 14 |
| 1321.82_2.739 | TRUE | 16 |
| 1332.9345_6.33 | TRUE | 1 |
| 1339.8437_3.087 | TRUE | 32 |
| 1378.109_6.06 | TRUE | 2 |
| 1413.0725_6.543 | TRUE | 10 |
| 1416.1569_11.643 | TRUE | 4 |
| 1417.1607_11.643 | TRUE | 4 |
| 1419.0884_6.065 | TRUE | 1 |
| 1424.1384_8.941 | TRUE | 2 |
| 1443.1241_6.536 | TRUE | 22 |
| 1460.0658_6.066 | TRUE | 6 |
| 1486.0818_6.746 | TRUE | 10 |
| 1488.1014_8.407 | TRUE | 3 |
| 149.0596_1.28 | TRUE | 66 |
| 1496.0473_7.572 | TRUE | 8 |
| 1512.0991_6.376 | TRUE | 1 |
| 152.0706_0.642 | TRUE | 56 |
| 1558.081_6.341 | TRUE | 1 |
| 1572.0922_6.339 | TRUE | 13 |
| 1580.9848_2.737 | TRUE | 7 |
| 1582.0806_6.324 | TRUE | 1 |
| 1583.0843_6.325 | TRUE | 1 |
| 1596.6247_6.715 | TRUE | 1 |
| 1602.3814_11.358 | TRUE | 36 |
| 1603.3857_11.359 | TRUE | 33 |
| 162.0914_0.651 | TRUE | 67 |
| 1628.3971_11.365 | TRUE | 15 |
| 1628.4765_11.515 | TRUE | 23 |
| 1629.4012_11.367 | TRUE | 8 |
| 1633.4321_11.51 | TRUE | 16 |
| 1636.2128_8.395 | TRUE | 2 |
| 1651.3848_11.251 | TRUE | 9 |
| 1653.489_11.522 | TRUE | 4 |
| 1654.4923_11.523 | TRUE | 4 |
| 1655.4167_11.377 | TRUE | 1 |
| 1657.5142_11.686 | TRUE | 20 |
| 1664.1589_6.841 | TRUE | 5 |
| 167.0854_1.05 | TRUE | 61 |
| 1676.3968_11.262 | TRUE | 2 |
| 1677.4008_11.264 | TRUE | 2 |
| 1678.4082_11.291 | TRUE | 3 |
| 172.1333_0.62 | TRUE | 46 |
| 201.0466_0.675 | TRUE | 64 |
| 203.0439_0.671 | TRUE | 65 |
| 207.1109_0.641 | TRUE | 1 |
| 219.1127_0.668 | TRUE | 66 |
| 229.0945_0.651 | TRUE | 67 |
| 230.073_0.636 | TRUE | 68 |
| 240.1585_0.692 | TRUE | 59 |
| 254.1387_0.695 | TRUE | 54 |
| 256.1695_0.637 | TRUE | 60 |
| 259.0944_0.697 | TRUE | 62 |
| 268.1541_0.91 | TRUE | 66 |
| 272.1651_0.645 | TRUE | 56 |
| 275.068_0.694 | TRUE | 66 |
| 275.1306_0.638 | TRUE | 66 |
| 277.1263_0.644 | TRUE | 3 |
| 308.1256_1.117 | TRUE | 3 |
| 308.1855_0.633 | TRUE | 20 |
| 312.1597_1.413 | TRUE | 1 |
| 315.1224_1.279 | TRUE | 48 |
| 320.2375_0.643 | TRUE | 69 |
| 337.1048_1.268 | TRUE | 6 |
| 346.1226_0.659 | TRUE | 68 |
| 353.079_1.243 | TRUE | 62 |
| 365.1356_1.988 | TRUE | 1 |
| 383.1533_0.931 | TRUE | 43 |
| 389.1625_0.672 | TRUE | 66 |
| 391.1601_0.671 | TRUE | 65 |
| 398.3413_0.639 | TRUE | 40 |
| 399.2641_1.416 | TRUE | 1 |
| 413.2797_1.415 | TRUE | 7 |
| 415.1686_0.643 | TRUE | 63 |
| 423.1686_0.697 | TRUE | 59 |
| 425.1649_0.679 | TRUE | 29 |
| 426.0969_1.177 | TRUE | 68 |
| 426.3213_1.384 | TRUE | 20 |
| 429.24_0.73 | TRUE | 3 |
| 430.2589_1.992 | TRUE | 11 |
| 432.202_0.638 | TRUE | 64 |
| 439.1646_0.682 | TRUE | 64 |
| 463.2323_1.708 | TRUE | 1 |
| 466.3162_1.004 | TRUE | 19 |
| 484.2854_0.667 | TRUE | 65 |
| 531.2196_1.712 | TRUE | 2 |
| 533.3253_0.69 | TRUE | 39 |
| 536.2833_2.749 | TRUE | 38 |
| 616.1763_0.902 | TRUE | 2 |
| 634.4366_0.676 | TRUE | 1 |
| 643.38_0.69 | TRUE | 6 |
| 664.5997_4.075 | TRUE | 41 |
| 682.5424_8.581 | TRUE | 6 |
| 682.5516_8.574 | TRUE | 4 |
| 706.612_10.9 | TRUE | 6 |
| 712.5953_4.577 | TRUE | 9 |
| 736.5043_0.684 | TRUE | 1 |
| 748.5833_7.03 | TRUE | 2 |
| 750.599_7.485 | TRUE | 3 |
| 764.6932_10.985 | TRUE | 2 |
| 766.5377_7.38 | TRUE | 1 |
| 766.6703_10.657 | TRUE | 13 |
| 785.5418_7.137 | TRUE | 4 |
| 786.6508_7.289 | TRUE | 1 |
| 794.7051_10.958 | TRUE | 5 |
| 798.564_5.251 | TRUE | 11 |
| 798.5655_5.067 | TRUE | 13 |
| 798.5661_4.516 | TRUE | 14 |
| 800.5792_5.005 | TRUE | 25 |
| 802.5954_5.397 | TRUE | 21 |
| 807.3448_6.714 | TRUE | 1 |
| 822.5634_4.827 | TRUE | 41 |
| 822.563_5.243 | TRUE | 36 |
| 822.6001_6.635 | TRUE | 2 |
| 822.6568_10.855 | TRUE | 1 |
| 826.5945_5.722 | TRUE | 44 |
| 826.594_5.988 | TRUE | 33 |
| 826.6326_7.901 | TRUE | 1 |
| 844.5121_3.103 | TRUE | 3 |
| 854.4963_2.738 | TRUE | 10 |
| 872.7324_11.418 | TRUE | 5 |
| 894.5999_5.902 | TRUE | 27 |
| 973.7251_11.18 | TRUE | 2 |
| 990.7559_11.04 | TRUE | 12 |
| 992.7701_11.13 | TRUE | 1 |
There are some features that are missing in a majority of subjects. I’m going to remove those because they may skew the data.
## [1] 2444
# Removing features missing from over 90% of data
omit_features <- contains_NAs %>%
filter(n/70 >= 0.90)
#preview
nrow(omit_features) # features to remove## [1] 19
## [1] 2425
# now remove these features from the omics dataset
omicsdata <- omicsdata %>%
anti_join(omit_features,
by = "mz_rt")
# check number of features now?
nrow(omicsdata)## [1] 2425
# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata
imputed_omicsdata[] <- lapply(imputed_omicsdata,
function(x) ifelse(is.na(x),
min(x, na.rm = TRUE)/2, x))
dim(imputed_omicsdata)## [1] 2425 87
Are there any NAs?
## [1] 0
# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample",
values_to = "peak_height") %>%
mutate(sample2 = sample) %>%
# add a new column with just subject
dplyr::rename("subject" = sample2) %>%
# remove the suffix from subject names
mutate_at("subject", str_sub, start=2, end=5)
# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
metadata,
by = c("subject" = "subject"))vignette for reference
Let’s look at what masses come up at each RT again
# rt vs mz plot
imp_meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "RT (min)",
y = "mz")There are some points that are at the same RT, meaning they could be coming from the same compound. We’ll run notame clustering to collapse features coming from one mass into one feature.
# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
cbind(imp_meta_omics$mz_rt) %>%
dplyr::rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
dplyr::select(c(mz_rt, mz, rt)) %>%
distinct() # remove the duplicate rows
# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
dplyr::select(-row_id) %>%
t())
data_notame <- data_notame %>%
tibble::rownames_to_column() %>% # change samples from rownames to its own column
row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column namesCheck structures
## 'data.frame': 2425 obs. of 3 variables:
## $ mz_rt: chr "189.1598_0.513" "104.1071_0.537" "184.0946_0.54" "162.1126_0.545" ...
## $ mz : num 189 104 184 162 204 ...
## $ rt : num 0.513 0.537 0.54 0.545 0.589 0.591 0.595 0.597 0.611 0.612 ...
## mz_rt 189.1598_0.513 104.1071_0.537 184.0946_0.54
## 2 x5109_b1_beta_c18pos_65 5393.7170 173277.2800 51759.0040
## 162.1126_0.545 204.1231_0.589 265.2403_0.591 144.102_0.595 118.0864_0.597
## 2 144957.5200 35302.4730 25206.2950 496116.2200 213544.1900
## 337.0619_0.611 132.102_0.612 623.1355_0.614 116.0707_0.623 645.1222_0.627
## 2 54898.4920 54694.3200 11556.9730 33922.2100 12947.2550
## 293.0984_0.628 100.0757_0.629 160.0606_0.629 353.0342_0.629 172.1333_0.62
## 2 669172.9400 35459.4900 98662.8200 26301.6210 39135.1450
## 120.0808_0.631 166.0861_0.629 258.1105_0.632 310.2012_0.633 188.0707_0.633
## 2 42466.1330 49830.1250 8127.8880 22907.9510 38425.3800
## 629.1522_0.633 308.1855_0.633 286.2013_0.633 331.0535_0.634 167.0891_0.631
## 2 27773.8100 1071.1624 37006.4730 115179.8800 6466.6787
## 247.1441_0.636 315.0805_0.637 256.1695_0.637 277.1263_0.644 332.243_0.64
## 2 19227.8870 835308.9400 503.6229 1397.2515 6094.1104
## 398.3413_0.639 585.2705_0.643 660.0815_0.643 265.1183_0.643 272.1651_0.645
## 2 503.6229 106325.0160 6281.2650 25677.8980 1143.5956
## 235.0925_0.646 152.0706_0.642 682.0633_0.646 207.1109_0.641 287.1003_0.648
## 2 21110.9260 503.6229 4690.7150 503.6229 14763.2780
## 314.2326_0.654 464.1914_0.656 293.1101_0.657 338.065_0.618 377.2467_0.663
## 2 98041.8100 2922.8655 13778.0100 9382.8500 14033.5670
## 328.2481_0.675 634.4366_0.676 316.2483_0.679 316.2608_0.679 340.2481_0.684
## 2 18997.6460 3395.8367 172184.1900 10606.5730 8409.4020
## 648.4519_0.687 692.4782_0.686 736.5043_0.684 533.3253_0.69 706.4934_0.689
## 2 5077.3500 4088.0354 2706.9158 503.6229 3497.0635
## 330.2637_0.689 515.3654_0.688 588.4113_0.688 368.2792_0.689 750.52_0.687
## 2 9743.1250 3182.8100 1957.7994 10114.1220 2877.0784
## 342.2638_0.69 508.3578_0.689 346.0093_0.691 240.1585_0.692 643.38_0.69
## 2 34116.9340 3075.1228 25780.4180 503.6229 1043.2233
## 356.2794_0.691 363.2166_0.691 254.1387_0.695 344.2792_0.693 205.1221_0.621
## 2 5844.3740 7206.6630 503.6229 7690.0176 5093.7275
## 259.0944_0.697 616.1763_0.702 425.1649_0.679 423.1686_0.697 429.24_0.73
## 2 503.6229 4483.4760 503.6229 503.6229 1073.3726
## 346.0093_0.919 616.1763_0.902 383.1533_0.931 344.2793_0.927 466.3162_1.004
## 2 22806.3960 2304.9036 503.6229 8477.9200 503.6229
## 167.0854_1.05 308.1256_1.117 286.1437_1.13 373.2483_1.147 353.079_1.243
## 2 503.6229 1220.9514 9559.3440 2644.5095 503.6229
## 337.1048_1.268 315.1224_1.279 585.2701_1.361 450.3212_1.383 426.3213_1.384
## 2 1094.0752 503.6229 16384.2050 4375.3020 1517.1168
## 413.2797_1.415 399.2641_1.416 312.1597_1.413 463.2323_1.708 531.2196_1.712
## 2 1245.3229 1691.5208 1938.8829 1366.6835 1879.2894
## 526.2644_1.714 424.3418_1.822 163.0753_1.983 365.1356_1.988 343.1537_2.002
## 2 2830.3435 12478.9500 1823.2700 1293.2098 1224.5826
## 430.2589_1.992 400.3418_2.122 542.3237_2.234 426.3575_2.274 518.3238_2.281
## 2 503.6229 20248.2400 15779.0860 25978.0980 10080.8030
## 468.3082_2.293 516.3057_2.488 494.3238_2.494 590.3212_2.604 568.3394_2.605
## 2 36834.3630 6834.4434 41801.2000 7841.7600 42264.5470
## 445.2944_2.63 566.3214_2.69 536.2833_2.749 544.34_2.697 482.3238_2.701
## 2 2599.1511 56812.7030 2144.9417 368163.4700 43307.0300
## 1087.6686_2.703 1085.6532_2.71 1063.67_2.714 1001.6557_2.715 526.2925_2.719
## 2 8532.2020 11860.8980 45158.1300 5241.0960 19726.0880
## 1128.671_2.722 1045.6241_2.726 578.3923_2.734 522.3458_2.735 1039.6724_2.735
## 2 3874.5076 4286.0070 8663.1660 104104.3360 141075.4400
## 1050.6573_2.737 1580.9848_2.737 637.4547_2.737 520.3416_2.737 854.4963_2.738
## 2 3425.4797 2591.2930 9267.7600 2110536.5000 1008.1475
## 1061.6537_2.738 558.2953_2.739 801.9871_2.739 1051.1585_2.738 802.4888_2.74
## 2 37508.3400 25146.8550 6660.1260 4586.0674 5628.0415
## 1077.6264_2.732 542.3219_2.742 1321.82_2.739 428.3732_2.759 603.2923_2.783
## 2 2352.1426 216674.7800 1340.7731 22848.5140 3954.6320
## 524.2743_2.81 502.2925_2.81 570.3547_2.836 500.2744_2.854 478.2926_2.855
## 2 9483.8800 45643.7200 18142.3830 12592.0380 59690.5820
## 508.3393_2.879 568.337_2.996 546.3554_2.996 1041.6857_3.056 1041.6842_3.059
## 2 8472.4060 19313.4860 96251.1400 7538.0103 7538.0103
## 518.322_3.062 534.2952_3.067 766.4889_3.074 765.9874_3.073 554.3925_3.077
## 2 261189.1200 29635.3340 12595.9060 14793.6910 14094.7330
## 515.313_3.077 762.9789_3.079 1002.658_3.079 1029.627_3.079 1003.1594_3.079
## 2 6508.7183 3456.1096 12248.1160 7750.6987 12764.0290
## 1018.1281_3.082 1250.8236_3.081 1013.6543_3.08 1011.1465_3.08 1258.8122_3.08
## 2 3244.3508 3285.5662 134102.8100 4763.7650 4368.2915
## 1010.6446_3.081 1508.9859_3.081 1261.821_3.081 1018.6298_3.081 496.6809_3.081
## 2 3859.5142 23519.3830 12869.5620 4046.2460 4457.2540
## 1509.9892_3.081 583.4441_3.082 597.4596_3.082 498.346_3.082 627.4702_3.082
## 2 21969.5570 12773.0040 8819.3120 195381.2700 10046.3690
## 1487.0039_3.082 991.6743_3.082 496.342_3.082 613.4547_3.084 519.3249_3.064
## 2 15511.9660 879353.2000 4294420.0000 17320.1840 74408.8700
## 1026.6606_3.098 1339.8437_3.087 1017.6873_3.095 1039.6688_3.106
## 2 3632.0415 503.6229 57205.2580 12549.9610
## 844.5121_3.103 454.2925_3.202 560.3108_3.237 522.3564_3.24 639.47_3.238
## 2 1817.0374 27750.6600 17206.4860 1083035.8000 5109.1450
## 1043.703_3.238 544.3374_3.238 1065.6846_3.239 805.0108_3.238 572.3709_3.254
## 2 42585.4100 173100.4400 13565.8600 3742.7310 11761.4570
## 502.2902_3.355 480.3082_3.357 480.3446_3.381 482.3601_3.403 504.3422_3.406
## 2 12590.9080 50793.1600 30530.7100 30982.6910 6784.8174
## 548.3709_3.408 510.3551_3.445 438.2976_3.525 508.3757_3.535 808.0344_3.778
## 2 13561.2680 46895.3120 13126.1870 19657.0180 9954.2350
## 582.4238_3.778 808.536_3.778 1059.2224_3.779 1069.7165_3.778 1571.0972_3.779
## 2 6841.0654 8679.4720 2830.6848 38131.3050 1797.1025
## 524.373_3.779 562.3265_3.778 1593.0791_3.779 1047.7355_3.778 1331.8987_3.778
## 2 2038640.1000 20471.2420 3281.9902 137818.2500 2004.7372
## 611.4752_3.779 641.486_3.779 526.3772_3.778 1012.7169_3.78 1594.0825_3.778
## 2 7494.6733 14114.6920 105510.5160 3057.3100 3136.2773
## 546.3534_3.779 614.3398_3.784 550.3862_3.859 482.3239_3.884 504.3061_3.884
## 2 276992.3800 11940.7840 14665.8130 47507.3120 17674.9080
## 585.2706_3.939 1191.515_3.94 607.2524_3.94 299.139_3.94 686.3908_3.94
## 2 146034.2000 16599.9630 52378.4020 24249.0720 6995.1753
## 672.3751_3.941 1169.5332_3.94 1213.4964_3.94 629.2342_3.942 508.3754_4.012
## 2 18887.1700 25512.0620 6557.7050 15115.1260 6429.1084
## 510.3914_4.038 538.3859_4.051 664.5997_4.075 466.3289_4.123 541.3858_4.202
## 2 14236.2070 5810.8555 503.6229 17835.5300 2574.6082
## 561.4122_4.23 521.4196_4.236 539.4303_4.244 563.4268_4.262 317.2685_4.264
## 2 23843.7360 33456.5350 10956.2450 12931.3390 7061.0900
## 552.4018_4.301 593.4768_4.312 563.4273_4.415 619.4811_4.462 798.5661_4.516
## 2 10377.7250 5784.8290 18468.0040 3092.1760 503.6229
## 593.4757_4.54 712.5953_4.577 569.4277_4.639 568.4266_4.64 543.4017_4.622
## 2 4907.9043 1515.6571 5953.4795 12313.4640 4421.9320
## 617.4748_4.643 593.4762_4.644 595.4929_4.645 577.4817_4.655 515.3703_4.774
## 2 41282.3480 5630.0254 18788.2730 6552.4790 1753.6527
## 774.5634_4.81 822.5634_4.827 596.4154_4.906 620.4935_4.951 317.2685_4.946
## 2 1473.5468 503.6229 2267.3196 8821.8080 13718.4030
## 641.4722_4.95 619.4903_4.953 647.5118_4.967 669.4937_4.965 800.5792_5.005
## 2 4980.5635 20838.7250 16498.7270 3707.5247 1091.9003
## 798.5655_5.067 695.5093_5.094 673.5278_5.092 669.5575_5.14 610.431_5.16
## 2 1007.2459 8457.6890 31376.8890 2030.2296 4867.0110
## 597.5086_5.242 822.563_5.243 798.564_5.251 592.4686_5.28 683.5094_5.286
## 2 4058.8694 503.6229 503.6229 4339.0366 3237.3774
## 661.5282_5.288 719.5696_5.337 802.5954_5.397 752.5219_5.513 608.4637_5.649
## 2 11441.7550 3824.0588 503.6229 2898.7860 4542.1787
## 1372.06_5.66 834.5932_5.658 765.5124_5.66 1350.0784_5.66 697.5255_5.661
## 2 1504.1250 7477.3240 10079.4390 2835.2773 106392.7800
## 675.5442_5.66 713.5011_5.661 755.4833_5.662 1035.2944_5.66 834.5933_5.658
## 2 386197.2200 3942.7058 6335.6216 2901.3713 8029.4736
## 754.5378_5.732 678.5059_5.698 826.5945_5.722 828.5522_5.728 860.609_5.81
## 2 3039.8936 5521.1553 503.6229 1888.6975 6816.4756
## 791.5279_5.817 1424.0918_5.818 739.5144_5.819 723.5413_5.819 1074.3179_5.819
## 2 10553.4450 3385.8970 5583.9480 144145.2700 3523.3270
## 1402.1101_5.819 1403.1135_5.82 1425.0951_5.82 701.5604_5.818 1073.8159_5.82
## 2 4824.2750 5107.7783 2573.1562 599996.9000 2832.2780
## 1227.7565_5.834 804.5524_5.837 826.5347_5.837 1249.7383_5.838 778.5376_5.842
## 2 12203.0250 12628.0010 3870.6897 8835.8190 12066.2390
## 800.5194_5.843 614.3823_5.849 780.5532_5.895 781.5567_5.896 894.5999_5.902
## 2 2907.3804 3563.6924 11540.6230 6917.9070 503.6229
## 796.5458_5.943 774.5637_5.944 518.4564_5.944 532.4721_5.945 699.5412_5.971
## 2 3604.9233 13888.0150 11549.3370 7264.8643 3503.8652
## 677.5586_5.973 826.5348_5.993 677.5582_5.973 826.594_5.988 776.5195_6.001
## 2 13189.3320 2856.2268 13189.3320 503.6229 11136.4220
## 754.5373_6.002 1378.109_6.06 689.5597_6.062 711.5408_6.063 870.596_6.054
## 2 40443.7230 1011.0738 200887.2800 50749.2400 1772.1250
## 1460.0658_6.066 1419.0884_6.065 721.5835_6.066 749.5557_6.066 727.5745_6.067
## 2 503.6229 1109.9097 9074.2630 5750.4565 19276.9770
## 847.6527_6.068 721.5846_6.067 730.5383_6.068 752.5201_6.07 768.4931_6.068
## 2 2905.4746 9074.2630 114104.4140 27510.7620 1770.2836
## 854.5686_6.074 719.5692_6.151 741.551_6.153 1024.6773_6.157 1046.6591_6.159
## 2 6152.8900 15576.8950 4452.8680 10911.2160 6503.9814
## 836.4936_6.219 846.5226_6.22 873.6685_6.266 778.5354_6.223 756.5541_6.24
## 2 4037.4954 4128.2330 2879.3850 25760.3340 110201.8400
## 852.5506_6.226 830.568_6.228 852.5508_6.228 715.5745_6.252 814.535_6.26
## 2 5595.4194 18959.2660 5595.4194 15226.3955 3462.7990
## 792.5531_6.261 1282.9156_6.311 860.4936_6.313 870.5225_6.318 1192.8093_6.322
## 2 12696.3280 1923.9395 6466.2800 9003.2150 2011.7031
## 887.5125_6.318 803.037_6.321 855.6216_6.322 838.606_6.327 802.5357_6.323
## 2 6353.0870 3670.3958 3051.4343 1869.0016 91769.5300
## 867.6575_6.328 897.6684_6.323 1583.0843_6.325 1582.0806_6.324 818.5091_6.325
## 2 2372.9265 11467.2400 1582.3990 1568.4613 4040.8977
## 1332.9345_6.33 1181.3124_6.332 780.5549_6.325 1560.0983_6.326 1572.0922_6.339
## 2 503.6229 1025.9532 368834.2000 4451.9185 503.6229
## 1561.1021_6.326 911.6839_6.326 1083.7892_6.33 791.5397_6.411 1536.0989_6.339
## 2 4407.1304 6154.8230 2970.0203 6074.7397 2921.4050
## 1180.809_6.327 1586.1131_6.353 1537.1023_6.339 815.5406_6.313 1558.081_6.341
## 2 1127.5221 3110.2422 2659.3777 3513.5120 503.6229
## 856.5843_6.351 1584.092_6.345 808.5757_6.369 1563.1161_6.369 806.5698_6.37
## 2 4378.5490 1198.4238 29549.9180 2475.0942 195438.1200
## 937.6995_6.371 807.5729_6.37 1612.1288_6.374 1613.1329_6.372 1562.1109_6.352
## 2 2721.3022 102868.9500 1763.7870 1275.6945 3185.3523
## 896.5379_6.373 828.5511_6.373 756.5542_6.373 923.684_6.374 1513.1028_6.383
## 2 4877.0825 45829.2930 125456.4500 6563.7100 1131.3215
## 778.5355_6.376 873.6684_6.375 1512.0991_6.376 887.6837_6.371 783.5732_6.445
## 2 29409.0310 4166.9194 503.6229 1988.4071 331912.1200
## 691.5742_6.403 1586.1128_6.4 862.5091_6.42 1587.1171_6.406 790.5352_6.424
## 2 3770.0908 4523.0825 5067.8040 4852.6760 10887.0890
## 872.5379_6.428 804.5511_6.435 768.5536_6.434 857.6371_6.438 1195.8324_6.435
## 2 8783.2360 111106.5100 55822.7970 5338.6836 2865.3325
## 820.5246_6.44 899.684_6.44 869.6734_6.444 1551.1176_6.447 765.0535_6.446
## 2 6667.6587 17594.6720 3180.4214 3467.1973 6213.0625
## 913.6995_6.445 1155.8333_6.446 1156.3359_6.446 1588.1268_6.447
## 2 8412.6800 3080.6865 4607.5050 6853.0015
## 1588.1294_6.449 1550.1138_6.449 782.5712_6.447 1536.6212_6.45 840.6219_6.452
## 2 6853.0015 4199.6777 678390.0000 1190.5923 6660.0550
## 884.6064_6.452 1589.1333_6.45 1538.1147_6.455 1536.1192_6.444 1564.1312_6.455
## 2 9413.5800 5112.1597 5550.9785 1409.7110 19175.4530
## 1565.1348_6.456 1539.1179_6.455 1496.1206_6.463 1496.6252_6.461
## 2 18705.8030 4691.4404 2144.1648 2699.3071
## 1116.8381_6.462 1497.1271_6.467 1508.122_6.465 1507.1178_6.465
## 2 8629.7590 2977.1853 10895.9680 13234.0550
## 1472.1218_6.472 862.6247_6.471 1468.623_6.472 1468.1206_6.471 1471.118_6.473
## 2 3205.9070 29764.3610 5167.5264 5868.0024 5601.3022
## 1509.1328_6.475 1457.6299_6.479 1486.1407_6.477 1457.1286_6.478
## 2 11411.8890 4680.1343 61512.7800 5138.5570
## 1485.1374_6.478 1527.117_6.481 1565.1864_6.477 1526.1136_6.481 1510.1375_6.48
## 2 60516.3240 6122.2560 4547.9770 6177.3384 9231.8545
## 1483.1167_6.498 704.4038_6.503 704.3938_6.501 704.5181_6.5 706.5843_6.5
## 2 3701.0376 2979.1597 3859.7725 5754.9610 62212.0100
## 703.8986_6.501 703.9516_6.503 705.6376_6.502 703.5779_6.502 704.5113_6.501
## 2 4795.3620 4313.9077 12838.2060 3894128.2000 4318.4080
## 1469.1118_6.499 1406.1439_6.504 834.7053_6.506 1448.1242_6.506
## 2 6197.3706 201903.2700 8837.9260 23742.5410
## 1418.1346_6.507 1447.1208_6.507 820.6896_6.508 1417.6332_6.508 1065.848_6.509
## 2 11429.7060 28251.1170 14094.6455 11612.3490 5100.8896
## 1066.3498_6.509 1469.1116_6.5 744.5538_6.51 1459.1207_6.512 1419.1345_6.509
## 2 7944.8300 6197.3706 130015.7600 21799.0680 4790.9395
## 1428.1239_6.513 1429.1272_6.513 1460.1239_6.512 1489.1055_6.52
## 2 38435.3200 39882.6250 19428.5960 3819.5625
## 1500.0993_6.518 1512.1013_6.516 758.5603_6.497 1444.1182_6.522
## 2 2863.6204 1807.7603 16470.0250 2425.5144
## 1451.0918_6.532 1077.3414_6.528 1411.1131_6.529 757.5569_6.495 1481.1047_6.53
## 2 1944.3883 19003.9920 4645.9330 54360.3800 4764.3140
## 1482.1063_6.531 1479.1431_6.532 1410.1118_6.532 741.5301_6.535
## 2 3807.7766 1477.4005 9581.1280 13782.2000
## 1463.0927_6.535 1413.0725_6.543 1443.1241_6.536 1431.0932_6.55 725.5571_6.544
## 2 1717.4335 1060.2296 503.6229 2014.5331 260720.4800
## 706.5381_6.548 1409.1106_6.553 1187.9262_6.553 776.579_6.552 823.6523_6.559
## 2 93600.5800 6711.3150 4113.9510 13343.0590 3093.4878
## 728.5196_6.564 794.5671_6.58 762.541_6.576 445.3676_6.577 795.5713_6.591
## 2 16527.3140 9425.6500 1313.9385 109687.9300 4672.0625
## 738.5064_6.603 1175.7015_6.637 1153.7198_6.64 822.6001_6.635 770.5692_6.675
## 2 2870.3188 6597.0938 11591.6800 503.6229 20639.8160
## 886.5092_6.697 1576.1291_6.701 751.5715_6.701 1192.8412_6.709 807.3448_6.714
## 2 6770.4907 2247.0337 44493.5550 4918.5146 2046.7420
## 1193.3431_6.71 1232.3347_6.716 1557.1328_6.71 1585.1303_6.711 1558.1357_6.713
## 2 5770.2754 7525.5645 4170.9136 2856.0566 3915.7880
## 818.0604_6.711 1458.1721_6.714 1585.6318_6.711 729.5912_6.714 1635.1157_6.712
## 2 4732.0010 2063.3494 2523.9658 306413.6000 17849.0180
## 1634.1123_6.713 923.6842_6.713 1536.1554_6.715 1535.1521_6.713 806.5725_6.713
## 2 15352.7360 32366.5020 17665.0300 17672.5080 1990778.8000
## 896.5381_6.715 1586.1251_6.718 1597.1201_6.716 893.6733_6.713 1623.6198_6.714
## 2 12253.8570 2667.9382 2787.4663 6461.1250 3466.6730
## 1596.1232_6.714 808.6366_6.716 937.6999_6.715 1612.1323_6.715 907.689_6.715
## 2 2560.0590 11384.0690 19629.9690 77630.0200 4811.0470
## 1624.1217_6.715 1613.1357_6.716 1596.6247_6.715 1231.8327_6.718
## 2 5639.5933 80207.5700 2719.5640 8712.6875
## 1540.1229_6.721 1597.613_6.72 1539.1194_6.72 1538.1166_6.72 829.5547_6.726
## 2 18157.9750 3647.1614 30808.1970 31225.4450 112573.4140
## 1462.1391_6.719 806.973_6.716 1624.6226_6.715 1461.136_6.721 1598.1117_6.724
## 2 3168.6880 1876.8628 3109.7778 3381.0615 3605.3027
## 844.5247_6.723 881.6374_6.724 1465.1023_6.726 1194.3346_6.723 1561.1011_6.726
## 2 12924.3340 8552.9310 3474.7380 4337.5340 5940.2400
## 732.5542_6.723 1560.0999_6.725 828.5517_6.726 1464.0995_6.726 863.6839_6.733
## 2 276055.3800 6536.0320 217082.3100 3964.6438 3554.8188
## 1486.0818_6.746 830.5605_6.803 1588.1294_6.745 819.6577_6.728 810.5875_6.727
## 2 1056.5840 36147.8950 6560.4404 2858.3442 16855.0160
## 864.6223_6.744 791.5433_6.743 849.6682_6.746 1570.1174_6.785 1571.1203_6.766
## 2 9567.5260 4449.9565 8039.0127 2437.0930 1673.1512
## 1232.8362_6.721 1564.133_6.867 752.574_6.803 1639.1441_6.788 764.5588_6.812
## 2 6162.0273 1592.2296 22218.8850 1738.5306 19950.1780
## 1389.0311_6.813 446.3707_6.814 829.5548_6.746 848.6162_6.816 1547.1209_6.83
## 2 2245.2970 15048.3770 112573.4140 1549.0123 1851.0347
## 445.3675_6.816 786.5403_6.817 1610.1103_6.827 816.5509_6.821 794.5677_6.822
## 2 44383.0470 13006.0770 2360.6768 12866.7840 23135.7680
## 770.5691_6.825 756.5471_6.823 752.5739_6.808 754.5356_6.825 830.5623_6.824
## 2 45051.0200 13398.2500 21862.4500 82551.0600 44440.2580
## 775.6384_6.825 1638.1373_6.816 751.5717_6.812 889.5277_6.826 792.5512_6.827
## 2 9095.5300 2424.0745 41401.2460 12069.7360 31182.0450
## 1189.3305_6.84 862.5095_6.827 429.3727_6.826 812.5558_6.826 740.5662_6.833
## 2 2290.8455 12648.2920 29734.8850 9023.7230 11398.9930
## 717.5903_6.828 827.6071_6.849 718.5932_6.828 725.5564_6.829 788.554_6.828
## 2 31804.7540 6890.6720 18248.2910 6774.0830 7176.1797
## 824.5758_6.83 1233.3393_6.794 703.5745_6.822 1546.1205_6.851 806.6235_6.835
## 2 5052.6846 2624.7170 17905.9220 2267.5580 8408.0940
## 1664.1589_6.841 1341.0272_6.83 1536.1083_6.846 802.5963_6.831 790.5738_6.832
## 2 503.6229 1807.0807 4108.0620 5939.1426 14675.8810
## 791.5771_6.835 870.5402_6.834 854.5671_6.833 762.5403_6.835 742.5356_6.834
## 2 10014.8450 5618.0310 95410.9400 5182.0020 11383.8750
## 1363.015_6.835 742.5728_6.833 1365.0287_6.836 800.5726_6.827 793.556_6.829
## 2 4216.7646 6285.1150 6167.1187 2866.0864 19775.4650
## 1339.0138_6.829 820.5836_6.837 755.6054_6.838 720.5531_6.843 949.6995_6.841
## 2 1964.2231 3550.4966 12663.0290 20646.0000 3175.7224
## 1207.3311_6.841 832.5855_6.839 740.5626_6.841 780.5519_6.84 963.7151_6.844
## 2 3592.2368 215492.3400 11398.9930 268613.7800 2147.8960
## 805.617_6.84 1170.3294_6.844 740.5609_6.842 727.5724_6.844 1169.3422_6.843
## 2 23781.8240 5499.6943 11398.9930 71935.2700 2549.3354
## 1208.3368_6.844 1221.342_6.847 796.5247_6.843 1208.8392_6.844 804.553_6.847
## 2 8168.1490 8990.7580 13196.6550 8508.4030 1490166.8000
## 1220.8404_6.847 1171.3299_6.847 822.528_6.849 1490.1152_6.846 706.5938_6.852
## 2 11420.0000 6874.2793 12749.6440 2711.4253 67258.3750
## 1637.1286_6.848 820.5251_6.85 1537.126_6.857 1550.1223_6.858 705.5909_6.851
## 2 4997.2275 56154.9450 5891.6943 4848.1133 164496.8400
## 1611.1156_6.852 1144.8407_6.848 1599.1159_6.853 1511.145_6.854 1601.625_6.853
## 2 4880.6240 2717.0547 4693.4136 3669.1072 4794.3223
## 1549.6231_6.854 1157.3434_6.852 1145.3428_6.85 1171.8322_6.85 1510.1351_6.854
## 2 3179.1572 6450.3070 2907.8354 9182.7550 3332.9200
## 1599.6188_6.853 766.5727_6.851 1183.8334_6.853 1509.1326_6.853
## 2 5672.6940 17519.1270 34739.0980 3473.4220
## 1600.1221_6.854 1184.336_6.855 1611.6196_6.855 1612.6219_6.855
## 2 7925.3870 26521.1100 4443.6200 3518.4224
## 1600.6232_6.854 1580.1025_6.858 1536.6252_6.856 1601.125_6.853
## 2 6881.6855 3157.4084 3068.8670 7206.6090
## 1536.1177_6.855 1195.8336_6.857 833.6399_6.878 1157.8451_6.853
## 2 4108.0620 68607.1800 5561.7715 7063.8403
## 1573.1314_6.859 1548.6249_6.858 1523.1232_6.86 1537.1276_6.858
## 2 2732.1280 4955.4800 2464.6990 5891.6943
## 1589.1303_6.863 826.6051_6.862 1562.1129_6.862 1574.6139_6.862
## 2 19960.3400 12937.0000 22721.0200 20510.0120
## 1581.0989_6.863 1553.1294_6.871 1575.1173_6.863 1587.1165_6.864
## 2 2159.3071 5932.7344 29707.4590 73110.5100
## 1586.1131_6.864 1487.1515_6.871 1563.1166_6.865 1575.6183_6.866
## 2 56394.6500 11787.8680 28489.4200 38242.0900
## 1592.5935_6.867 1563.6184_6.864 1514.1154_6.867 1576.1212_6.867
## 2 3451.8123 17199.9920 11699.0390 35512.4450
## 1548.1292_6.867 1572.126_6.868 1580.5988_6.86 840.6221_6.868 1616.1518_6.868
## 2 6371.3115 2966.9448 2407.3428 19593.6020 13179.2820
## 1615.1494_6.868 784.5042_6.876 785.0201_6.879 1593.594_6.87 1594.0976_6.869
## 2 24269.1150 6912.2266 7153.8850 3847.8716 3865.2078
## 1502.1143_6.871 1614.1454_6.868 784.7847_6.875 1564.621_6.871 857.6374_6.871
## 2 4114.4297 23931.6930 22815.4160 15694.0110 15725.6570
## 784.4182_6.876 784.3687_6.876 783.1226_6.878 783.4174_6.877 1593.0936_6.87
## 2 5728.1390 5230.8975 5396.5073 9845.5520 3322.1904
## 1515.1185_6.869 913.7001_6.872 1487.1511_6.872 783.1504_6.876 785.5792_6.874
## 2 12058.3870 55061.8550 11787.8680 5308.9165 142187.2500
## 899.6847_6.873 783.3656_6.875 784.779_6.877 782.9105_6.881 784.6308_6.874
## 2 77207.8800 9307.4820 22815.4160 9752.2200 31362.0040
## 783.332_6.875 783.4393_6.874 783.3243_6.874 783.2199_6.876 784.3942_6.875
## 2 7112.8164 9775.9930 9051.5180 6609.1133 5407.6475
## 1577.1237_6.867 784.4291_6.877 783.1601_6.876 785.0064_6.878 783.1327_6.878
## 2 17270.2850 6519.6690 5370.0770 7101.7363 5553.3584
## 782.5732_6.875 783.2011_6.878 1488.1552_6.873 783.5024_6.875 783.2594_6.875
## 2 5118051.5000 5738.1084 10344.3640 16438.4000 8841.0060
## 783.2926_6.876 783.431_6.876 783.2519_6.875 783.2153_6.875 783.3975_6.877
## 2 8879.7370 11415.3190 8841.0060 6609.1133 9822.5610
## 783.3843_6.877 784.4472_6.876 794.0606_6.876 783.4057_6.877 785.0389_6.88
## 2 7887.0366 7024.9863 10706.0180 9822.5610 7854.3130
## 785.049_6.877 783.2313_6.877 784.4911_6.877 786.5814_6.876 782.9653_6.878
## 2 6642.3755 7012.7370 7622.0440 29706.6050 6474.7227
## 783.067_6.877 784.5306_6.877 783.1442_6.879 783.0991_6.879 784.3802_6.879
## 2 5600.5674 7788.1030 5385.0420 4890.1500 5426.7188
## 784.3306_6.878 782.9942_6.879 783.3522_6.877 783.1872_6.878 784.3478_6.878
## 2 4889.5190 5416.9478 7914.3000 6745.3380 6518.7840
## 783.3414_6.876 1562.1131_6.863 784.4086_6.876 782.5279_6.881 784.3757_6.877
## 2 8348.8110 22721.0200 5975.5410 14215.5460 5426.7188
## 1590.1432_6.882 1549.1336_6.872 784.9793_6.877 1548.1302_6.87 1566.2163_6.888
## 2 22002.9630 6424.1000 8130.7410 6655.7446 22692.1950
## 1564.1347_6.888 897.7049_6.893 1569.1509_6.94 883.6894_6.894 869.6738_6.894
## 2 850852.2500 6647.4990 23776.1930 17272.9080 20351.4360
## 1525.1338_6.906 1565.1377_6.892 784.6324_6.878 1592.1507_6.918
## 2 2977.8179 861451.7000 31362.0040 12488.2100
## 1552.1254_6.917 1463.1508_6.915 1172.8405_6.921 1172.3364_6.884
## 2 12711.5050 2801.7778 10840.8470 9370.1010
## 1551.1225_6.925 1550.14_6.908 782.0605_6.928 1591.1484_6.916 1551.6195_6.93
## 2 9737.1880 4848.1133 8147.8960 22023.9100 11321.2010
## 1540.1336_6.931 1542.2142_6.931 1541.137_6.931 1541.2129_6.933
## 2 486983.6600 11582.8310 465805.6000 18545.8790
## 1524.1325_6.944 1160.8402_6.939 1539.62_6.944 1160.3363_6.993 890.7034_7.007
## 2 3996.6003 12344.8990 13607.3100 11612.7120 24366.7950
## 846.6771_7.001 876.6878_7.013 759.4494_6.981 759.2363_6.979 760.4788_6.981
## 2 14237.4900 26663.7320 13573.4520 9096.3140 8931.8230
## 759.4107_6.98 873.7049_6.978 760.3659_6.98 759.1825_6.981 761.2418_6.982
## 2 12453.7660 8745.5550 7240.0250 8073.8936 7632.6235
## 761.5791_6.979 758.6931_6.98 760.5104_6.985 759.3448_6.983 758.5734_6.982
## 2 159828.1700 151150.4800 10110.6830 12389.0630 5924760.5000
## 759.3553_6.982 759.5018_6.981 759.317_6.981 759.3092_6.981 760.4052_6.983
## 2 11545.6080 19481.5640 11961.7450 11055.1080 7653.5890
## 759.3828_6.982 760.4925_6.98 759.4259_6.982 759.3339_6.982 1516.1361_6.982
## 2 13261.5640 9171.5510 12957.3940 12457.1140 1410985.4000
## 759.2062_6.983 759.391_6.982 758.9593_6.983 759.2797_6.981 760.6287_6.982
## 2 9527.5670 13356.7440 7871.1600 10960.3310 37455.2970
## 760.421_6.983 759.2558_6.98 760.5281_6.984 761.2551_6.982 1517.1828_6.983
## 2 8018.0870 10437.2280 8964.4430 7059.6700 70068.8050
## 759.2209_6.98 760.3904_6.983 759.1347_6.985 758.9871_6.983 759.2864_6.986
## 2 8099.9546 7451.4630 7856.2646 7519.8735 10960.3310
## 759.1729_6.983 759.1221_6.983 759.0903_6.983 759.243_6.983 760.4378_6.981
## 2 8494.0900 8485.6700 9391.7260 9508.1700 7236.2650
## 859.6895_6.984 761.0316_6.983 759.1094_6.983 760.5329_6.983 759.058_6.985
## 2 23681.3400 9723.2900 7969.4097 9791.5930 7774.2285
## 758.5284_6.987 759.4041_6.982 760.9991_6.984 760.3462_6.982 760.2985_6.984
## 2 16056.2940 13356.7440 10946.2920 6063.8203 5087.1553
## 759.2652_6.984 759.0221_6.985 760.7903_6.983 760.2926_6.982 759.1449_6.983
## 2 10437.2280 7333.5530 25374.4570 5774.9730 6841.0490
## 760.3968_6.983 760.3759_6.985 760.6318_6.982 1518.2168_6.99 748.5833_7.03
## 2 7653.5890 7195.2363 35895.7270 27325.6560 1124.0197
## 761.0061_6.983 1528.1243_7.002 1148.8422_7.002 1527.6219_7.002 760.5406_6.984
## 2 10946.2920 18321.8710 18726.2700 16791.1300 8467.7660
## 1475.1518_7.004 845.6738_7.007 1476.1552_7.005 889.7001_7.009 769.5595_7.011
## 2 7617.8633 23587.6600 8129.0005 40159.9000 14592.2690
## 770.0615_7.009 1564.1269_7.052 718.5934_7.015 717.5903_7.018 875.6845_7.017
## 2 14226.6720 9514.4980 18629.5940 38487.5270 49967.1400
## 1523.0877_7.024 1478.1161_7.023 1522.0846_7.024 1479.1185_7.024
## 2 11077.4370 5792.9300 12143.6690 4028.0125
## 1568.1537_7.025 1567.1512_7.025 1538.1135_7.026 1539.117_7.026
## 2 73506.9200 138715.3100 38153.0500 43548.2500
## 1566.1476_7.026 1553.1308_7.035 1159.3314_7.035 764.5222_7.036 720.5532_7.039
## 2 146112.4200 8087.8223 9756.1900 24172.5860 14088.0380
## 1550.1462_7.021 1552.6273_7.034 1159.8332_7.037 1544.0675_7.037
## 2 3413.4805 8553.4920 15630.4540 4475.8433
## 1545.073_7.035 833.637_7.039 1173.8476_7.044 781.62_7.043 1572.101_7.045
## 2 3244.0056 7982.1294 6445.1055 5688.7090 2317.2297
## 780.5518_7.046 1173.3456_7.052 811.5943_7.054 1184.8408_7.054 809.5892_7.057
## 2 183253.7200 5460.8657 19779.6040 8766.1340 291041.7800
## 1617.1644_7.055 808.586_7.055 786.504_7.061 1616.1606_7.056 1578.1434_7.057
## 2 11359.0870 588403.0600 3929.7473 11933.2320 6161.7120
## 792.5893_7.058 858.5992_7.06 939.7152_7.063 848.5388_7.066 1185.344_7.069
## 2 15391.7010 2558.9810 7418.6600 9512.5550 6107.9320
## 925.6997_7.067 1172.8404_7.074 830.5667_7.072 1197.3456_7.08 1578.1423_7.058
## 2 11909.1820 3174.1330 70597.1900 3549.9673 6161.7120
## 1593.1638_7.133 1577.1364_7.091 1564.1272_7.069 794.0604_7.102 793.5586_7.102
## 2 20253.2640 2604.5760 9514.4980 7002.1370 6752.8570
## 1565.1315_7.095 1577.6354_7.106 731.6047_7.112 1545.1554_7.122 1185.8481_7.12
## 2 7596.2305 2974.8100 5290.3833 7436.1274 7499.9090
## 1186.3509_7.119 1174.8563_7.123 1543.1503_7.123 1542.1466_7.121
## 2 5486.9070 3801.6438 33650.1500 35511.2500
## 1566.6417_7.129 1544.153_7.124 1590.143_7.137 1506.089_7.132 1614.1417_7.109
## 2 2229.7130 18147.6930 14288.6090 1726.0093 4281.4746
## 871.6892_7.141 1592.1596_7.145 1594.1676_7.138 785.5418_7.137 1507.0917_7.135
## 2 8669.5390 26156.4300 11131.2230 2624.5994 1453.5707
## 1187.8642_7.137 1579.649_7.139 1187.3609_7.137 885.7047_7.143 842.6373_7.146
## 2 3963.2363 3174.7930 3975.4958 5349.6465 8487.8270
## 1568.1618_7.14 1591.1473_7.14 1580.1526_7.144 834.6001_7.175 784.5879_7.141
## 2 64757.8750 15067.0070 3506.3062 24930.0000 1692172.1000
## 1198.856_7.141 915.7154_7.143 901.6999_7.143 787.5943_7.141 807.5705_7.147
## 2 8771.7010 17416.8220 30570.2270 41582.9800 99010.7100
## 1569.166_7.14 822.5404_7.148 806.5675_7.192 1574.148_7.172 1575.1514_7.174
## 2 62605.8000 10181.6010 197554.6900 3456.5688 2419.8360
## 859.6529_7.212 925.6996_7.166 445.3675_7.185 1530.1458_7.188 1531.1493_7.186
## 2 8776.9590 8828.3290 12413.9570 6399.9870 4770.6800
## 874.5538_7.197 692.5581_7.191 790.5744_7.193 1598.1451_7.197 1554.142_7.183
## 2 12562.0710 7282.0550 32107.7170 1604.7759 2480.2630
## 831.57_7.208 812.5561_7.198 907.6886_7.2 746.5692_7.204 830.5666_7.214
## 2 28144.8570 7984.7334 1989.6963 95167.7200 48927.3360
## 768.551_7.206 748.5272_7.208 842.5663_7.213 820.5846_7.213 864.5249_7.21
## 2 16492.4940 5183.9150 6109.2880 24081.3870 6938.9453
## 827.6266_7.218 1548.0963_7.222 762.5038_7.219 740.5222_7.221 743.6054_7.227
## 2 3752.6655 3244.9707 13752.5580 42976.8600 7719.2812
## 1546.0812_7.232 1547.0845_7.233 856.5819_7.24 1524.0983_7.244 1525.1027_7.243
## 2 3483.6980 3488.1213 6638.0005 5453.5280 4776.9340
## 807.5706_7.257 834.6002_7.257 724.5272_7.284 797.5882_7.268 842.638_7.268
## 2 118741.4600 39144.8440 1910.8232 35788.4500 10514.0000
## 796.5845_7.27 1542.1462_7.263 1581.1632_7.283 806.5675_7.265 874.5539_7.248
## 2 72564.8600 10236.8920 12460.1220 221178.7000 12604.8080
## 1566.1444_7.237 859.6529_7.266 822.5405_7.274 807.0682_7.274 1604.1571_7.283
## 2 6496.6960 9165.1560 13790.8620 14134.1330 4398.7830
## 902.7033_7.285 1198.8563_7.281 1187.8649_7.285 1580.159_7.285 901.6999_7.285
## 2 22156.0230 15122.6640 8691.5720 16503.8630 43110.0430
## 1590.144_7.285 1579.6508_7.286 1199.3591_7.285 1602.1511_7.303
## 2 25340.2340 6353.3730 13333.3610 3956.8882
## 1619.1782_7.285 1187.3625_7.286 1591.1476_7.288 784.5881_7.29 871.6892_7.289
## 2 6223.6963 5950.3315 27671.9960 2985055.5000 12021.5920
## 788.5964_7.29 1569.2427_7.289 1569.1673_7.289 786.6508_7.289 785.452_7.291
## 2 13002.8090 7227.2820 169802.7700 14278.9120 3061.6868
## 1568.1636_7.29 915.7155_7.29 885.7047_7.291 787.5945_7.291 916.7188_7.29
## 2 183332.1200 26279.4590 9076.5380 69423.9300 16018.3640
## 784.9268_7.293 1591.6495_7.292 1199.8618_7.293 1602.6436_7.296 1500.102_7.296
## 2 3556.9736 7716.3160 8550.2400 3842.6375 8130.5380
## 1592.1607_7.297 1501.1056_7.297 1522.0815_7.299 1618.1731_7.3 1603.1486_7.299
## 2 53719.4200 6924.1850 3485.3882 7398.9080 6123.0910
## 716.5222_7.301 1593.1654_7.298 1210.8558_7.304 1210.3541_7.309
## 2 29371.9920 48039.5700 6494.1220 5208.9634
## 1614.1426_7.311 1615.1461_7.313 1616.1569_7.316 1211.3587_7.312
## 2 6480.6406 6684.4070 6791.0840 6283.5860
## 808.5857_7.318 795.5728_7.343 1617.1623_7.316 1557.1645_7.322 1556.1613_7.323
## 2 326688.6000 13748.4850 5146.9920 9951.8450 12318.7780
## 1616.1572_7.316 809.5888_7.318 939.7152_7.328 1601.1673_7.328 1600.1629_7.331
## 2 6791.0840 164733.8000 4276.1560 2296.2922 2488.0470
## 810.5975_7.409 925.6999_7.337 1577.1677_7.349 816.589_7.342 830.5666_7.346
## 2 71589.9800 6891.4443 4408.1323 9590.6030 40156.2030
## 1576.1637_7.357 792.5899_7.352 909.7046_7.351 814.5717_7.361 766.5377_7.38
## 2 4759.7980 52424.3240 3424.4446 10382.2705 27209.4060
## 1550.1483_7.401 1574.1454_7.378 719.6006_7.392 1572.1304_7.394
## 2 4758.8804 2446.9675 4816.7130 1660.7784
## 1524.1333_7.404 824.6209_7.408 1551.1527_7.401 1562.1493_7.418
## 2 1292.6350 2627.0017 5710.2900 2471.5918
## 1550.1497_7.403 718.5737_7.412 1533.1433_7.416 788.5563_7.415 766.5755_7.417
## 2 4758.8804 8682.3580 3260.2266 57011.7700 263404.9700
## 1532.1401_7.417 1562.1488_7.418 1563.1535_7.418 897.7047_7.419 883.6892_7.419
## 2 2746.2073 2471.5918 2596.2554 6101.3110 12916.7140
## 818.5668_7.42 1554.1324_7.421 796.5848_7.423 1577.1679_7.401 1576.1634_7.391
## 2 22529.6520 1357.0139 107920.9800 2470.9812 3383.8892
## 1539.1546_7.459 927.7143_7.45 812.6068_7.457 832.5817_7.451 811.6039_7.456
## 2 2163.3127 6838.9920 22156.7340 31705.1050 73096.5300
## 810.6006_7.457 764.5195_7.458 742.5377_7.462 1538.1514_7.472 881.5145_7.469
## 2 134224.0800 7721.0737 22778.2030 2572.7212 6062.2285
## 859.5323_7.471 840.5868_7.472 876.559_7.475 818.6053_7.48 1497.1674_7.486
## 2 5798.9062 8956.4600 11421.3570 37945.4260 1581.1414
## 807.6359_7.483 750.599_7.485 1583.178_7.489 1582.1737_7.485 1583.1777_7.489
## 2 3831.0168 503.6229 2480.3840 2310.0151 2480.3840
## 1508.1468_7.513 794.568_7.494 889.6996_7.495 1541.1852_7.515 772.5855_7.501
## 2 3585.3577 38519.9100 7052.8890 3199.9272 226832.1600
## 1500.1349_7.508 1509.1474_7.507 753.5881_7.505 1544.1615_7.508
## 2 2126.2856 2092.0752 85613.6950 4649.3955
## 1545.1646_7.511 1503.1775_7.514 1504.1822_7.513 822.5994_7.509 731.6073_7.511
## 2 4911.4050 6527.6740 3927.5327 13095.0530 454151.2800
## 1463.2071_7.511 1462.204_7.513 1515.1602_7.514 1514.1517_7.514
## 2 3124.4854 3205.0034 4324.0240 4498.1920
## 1564.1662_7.508 1473.1717_7.517 1474.1752_7.518 859.6891_7.519
## 2 2016.1738 4781.7980 3421.7024 8154.2960
## 1507.1493_7.519 1506.1473_7.519 764.5566_7.52 873.7045_7.52 1508.1474_7.516
## 2 6633.2515 6062.3270 31794.8850 4982.9850 3585.3577
## 1487.1502_7.525 1485.1447_7.523 742.5753_7.523 1518.1461_7.531
## 2 2613.7031 2148.8591 191131.1200 2861.4202
## 1484.1411_7.523 1486.1496_7.524 1465.1678_7.525 1466.1708_7.525
## 2 2470.0022 1497.9453 6654.8980 6344.9556
## 1477.1393_7.527 1523.1825_7.536 1476.1365_7.528 1553.1686_7.544
## 2 6809.4550 2414.2344 6057.2340 3506.8452
## 1526.1519_7.532 1534.154_7.527 1527.1556_7.532 1541.1773_7.535
## 2 4657.5740 2007.2792 3840.5676 3371.8267
## 1518.1432_7.534 1468.1317_7.534 1535.1558_7.533 734.5702_7.535
## 2 2861.4202 6072.6187 2250.0254 353520.0300
## 1469.1342_7.535 865.6997_7.537 851.6839_7.537 756.5512_7.537 821.6735_7.538
## 2 5004.6157 5414.0570 10778.7380 54331.4180 4345.4893
## 1479.137_7.538 1490.1052_7.54 1540.1625_7.542 804.5937_7.544 794.5862_7.544
## 2 2990.7666 2127.0884 3679.2427 3675.0496 15818.0250
## 1510.1552_7.549 1511.1587_7.551 1482.0893_7.554 1552.1644_7.555
## 2 4226.1150 3603.8474 2527.3809 3953.8550
## 835.5327_7.555 1499.1861_7.555 852.559_7.556 792.5899_7.556 1502.1505_7.558
## 2 4495.5090 5060.1533 7433.6323 79604.4140 8881.8440
## 1503.1576_7.556 1500.1884_7.555 857.5144_7.562 1495.1496_7.564 849.6468_7.572
## 2 8585.5010 4000.7683 3715.0370 2497.3570 1906.3047
## 1561.1726_7.566 814.5717_7.564 748.5274_7.569 1496.0473_7.572 1537.1733_7.573
## 2 2592.5684 13302.2200 33318.0080 503.6229 6685.3300
## 1517.1166_7.575 1516.1139_7.574 1560.169_7.579 1536.1705_7.575 770.509_7.576
## 2 2530.5850 2080.4326 2282.4321 6401.7490 9558.0200
## 768.5909_7.579 1578.1799_7.626 899.7204_7.586 835.6299_7.591 790.572_7.588
## 2 425445.9700 4914.7740 8112.9507 3593.3400 71291.9200
## 885.7047_7.587 1494.1454_7.592 806.5656_7.605 1512.1627_7.554 786.5917_7.604
## 2 18110.2000 2098.2405 15177.7750 2484.3500 12606.6910
## 1491.1814_7.626 795.6347_7.605 1579.1825_7.633 832.582_7.631 941.7308_7.633
## 2 2028.2305 4027.3157 5770.3623 32799.2770 3788.4329
## 902.5744_7.633 812.607_7.646 811.6042_7.645 810.6013_7.646 1578.1793_7.636
## 2 4986.0996 33542.0430 107926.2800 204152.2500 5323.6300
## 850.5541_7.648 1544.1549_7.686 1592.1591_7.656 1181.7509_7.659
## 2 13692.6510 23416.9530 3815.0010 3838.7810
## 1187.8641_7.656 818.6355_7.665 1545.1606_7.669 744.5901_7.661 816.587_7.665
## 2 3432.4690 8476.7350 9823.2010 122544.6400 23278.8140
## 1526.1435_7.632 1528.1659_7.665 1529.1702_7.665 782.5674_7.669
## 2 3219.6997 13098.2320 9945.9770 204366.1700
## 1605.1979_7.674 1571.181_7.67 1570.1764_7.672 1570.1773_7.672 911.7203_7.674
## 2 3559.1594 25507.2810 26085.3030 26085.3030 8083.8223
## 1604.1948_7.675 798.5402_7.677 1163.3585_7.687 1538.1799_7.62 1539.1868_7.674
## 2 4078.1277 12759.4400 12428.9020 3300.3145 3109.0042
## 1505.1707_7.678 1576.1618_7.678 1504.1669_7.68 878.7033_7.717 925.736_7.685
## 2 20141.2340 4844.1074 22547.9220 25510.8300 4708.0386
## 1558.1829_7.689 1162.8568_7.691 794.6062_7.691 1558.1819_7.689
## 2 7927.0690 16056.3570 297145.3800 6884.3910
## 1588.1988_7.697 1548.6599_7.695 1589.2021_7.7 1543.1477_7.702 1554.1833_7.701
## 2 4674.9470 3720.1523 5487.4917 43559.9200 71208.0300
## 1571.1809_7.671 1555.1868_7.702 1542.1448_7.703 1546.1714_7.763
## 2 25507.2810 70354.0300 40527.9020 6810.4053
## 1543.1481_7.702 1151.8656_7.71 771.5762_7.712 1531.6527_7.711 1151.3639_7.711
## 2 43559.9200 17830.4570 17433.5570 13362.9770 11389.1040
## 1544.1542_7.691 761.302_7.716 1532.1582_7.713 861.7048_7.713 877.7_7.713
## 2 23416.9530 4584.8213 12936.2470 20699.1840 49098.1300
## 1542.645_7.698 760.9218_7.716 1510.1192_7.742 762.5251_7.716 761.5191_7.716
## 2 9268.2520 6645.4194 2446.1653 5528.1370 10925.8580
## 847.6893_7.716 761.3781_7.717 762.6491_7.715 760.9763_7.718 761.4062_7.717
## 2 24082.5180 5340.2750 22571.0400 4674.2236 6146.4950
## 761.329_7.717 761.0763_7.717 761.4545_7.719 760.5883_7.719 762.5433_7.717
## 2 5142.3794 3327.1802 6169.5625 4489468.0000 5709.7173
## 761.4437_7.717 761.1927_7.717 763.5945_7.717 1521.1684_7.718 1520.1651_7.717
## 2 6169.5625 4225.2026 111291.4000 488071.2000 529120.6000
## 891.7156_7.718 761.3681_7.718 1531.1683_7.726 1535.1253_7.718 1534.1209_7.719
## 2 34318.6200 5340.2750 6923.9043 5424.9260 6476.6000
## 761.2595_7.716 761.4141_7.718 760.5459_7.719 761.3174_7.717 1583.089_7.72
## 2 4221.0815 5045.6753 11911.6570 4070.7178 7703.6436
## 1584.0928_7.721 762.5032_7.719 1521.243_7.719 1629.2009_7.725 1628.1971_7.724
## 2 7824.3150 5498.3190 19060.6230 6422.5650 6269.8535
## 774.5423_7.725 1608.1354_7.743 1568.1599_7.728 1542.1456_7.705
## 2 11221.3160 1272.1986 5344.1250 40527.9020
## 1568.6597_7.736 1569.1611_7.737 1569.1618_7.737 1595.1823_7.739
## 2 6495.0503 7725.8887 7725.8887 98361.6800
## 1594.1789_7.739 1588.1975_7.7 1188.8722_7.741 1597.1881_7.738 1579.6524_7.748
## 2 107510.5860 4674.9470 6300.3110 22405.0430 4470.2580
## 1580.1749_7.748 835.655_7.749 834.602_7.751 1669.196_7.752 1668.1927_7.752
## 2 5792.3900 11716.9440 448794.2200 7539.9023 7337.1504
## 1616.16_7.754 1617.1626_7.754 1199.864_7.757 1518.2006_7.758 1517.1985_7.759
## 2 7962.7446 8248.7310 6011.0376 14714.3430 14677.2840
## 965.7308_7.761 1540.1703_7.761 1591.2114_7.764 828.6106_7.766 1592.2118_7.764
## 2 4966.2070 5146.0073 2566.1387 6108.8945 3137.5660
## 818.6356_7.752 819.5737_7.769 951.7153_7.77 1531.181_7.771 1530.1768_7.745
## 2 10548.5900 5129.0654 8069.8920 7283.2686 7336.8540
## 757.6221_7.776 856.5823_7.778 798.5402_7.73 1484.1047_7.781 1559.1256_7.781
## 2 173236.7200 37189.6800 12759.4400 12958.6980 3212.6826
## 1558.1201_7.783 1485.108_7.781 1546.1749_7.78 1604.1937_7.739 780.5921_7.79
## 2 3719.1820 12755.6810 6810.4053 2881.0300 21050.6540
## 1506.0868_7.79 1547.1793_7.789 1507.0899_7.79 1580.1032_7.792 837.614_7.764
## 2 7164.4497 6038.4316 6378.9697 1895.6180 20684.0250
## 1481.1402_7.792 858.5914_7.792 825.6473_7.81 821.6234_7.811 820.6201_7.811
## 2 1800.8228 7071.5520 3993.2249 12139.4410 22466.3280
## 902.5743_7.805 724.5277_7.806 1448.0465_7.808 1470.0283_7.811 770.6049_7.812
## 2 5288.8380 82317.8750 2385.7983 1235.0146 53227.2660
## 825.6476_7.811 802.5717_7.81 746.5091_7.814 811.6292_7.819 770.6048_7.813
## 2 3993.2249 3054.8853 22823.3730 9954.6150 53227.2660
## 798.6003_7.83 860.6149_7.833 792.5888_7.837 1532.1012_7.882 820.5823_7.852
## 2 34042.7030 6221.1810 18303.9510 2098.2295 8507.3650
## 783.6368_7.847 805.6187_7.848 745.6202_7.852 818.6049_7.793 806.611_7.876
## 2 31443.3180 9091.6620 13998.8010 8690.4080 4951.1753
## 876.5695_7.88 878.5742_7.89 756.5904_7.889 748.5844_7.89 772.5245_7.894
## 2 7075.7820 5127.9660 9227.3960 18335.3610 9190.0830
## 1547.1803_7.856 700.5274_7.902 1486.1173_7.882 826.6326_7.901 820.6202_7.907
## 2 5399.4360 21380.6050 3084.0728 1115.1819 24864.7500
## 1578.178_7.924 854.6305_7.919 900.5692_7.918 1511.123_7.922 1510.1192_7.922
## 2 2589.2476 4165.8470 8471.4650 4173.4316 4908.7837
## 1558.1745_7.924 733.6212_7.929 1566.1816_7.932 734.6246_7.927 733.6215_7.929
## 2 3018.6914 26322.7480 1988.4016 13073.2260 26322.7480
## 1567.1909_7.931 1570.1778_7.936 848.556_7.937 885.6686_7.937 1544.2137_7.94
## 2 2143.8262 10944.2300 11359.5360 7438.5767 3019.1958
## 832.5829_7.939 1543.2104_7.94 822.5985_7.942 750.5434_7.943 1582.1177_7.942
## 2 190289.3000 2826.2402 7101.0186 42114.6330 6270.0050
## 1238.3816_7.948 1604.1947_7.949 1592.1862_7.949 1237.8801_7.949
## 2 11928.0460 5575.0215 3663.0583 12679.1630
## 811.0052_7.959 1642.1751_7.953 1643.1784_7.953 1605.1986_7.95 1561.1392_7.955
## 2 4200.2954 22001.8050 26935.0800 5968.8647 15708.4380
## 812.6663_7.96 822.0921_7.956 1227.3893_7.956 1560.1357_7.954 1632.1881_7.956
## 2 20483.7340 13487.0800 8973.3570 17124.4300 12940.0440
## 1591.1826_7.956 927.7157_7.955 812.5189_7.959 1226.887_7.956 821.5898_7.956
## 2 3305.5847 47009.2540 3487.0654 13389.3250 12952.9170
## 813.6106_7.959 1225.88_7.956 1226.3841_7.957 941.7313_7.957 1631.6823_7.958
## 2 105242.3700 9936.5750 11729.9080 34206.3320 12720.3600
## 1622.2784_7.959 897.705_7.958 911.7206_7.958 811.2977_7.959 810.9483_7.96
## 2 8327.5340 16258.5050 12577.9060 2953.2380 8446.5330
## 810.6041_7.959 1590.1749_7.958 1621.1991_7.959 811.4149_7.961 814.6127_7.96
## 2 3797555.0000 2573.6409 291290.3400 3379.2712 19150.5780
## 1620.1954_7.96 811.5374_7.96 811.4537_7.958 1621.2757_7.96 1631.1866_7.959
## 2 288335.1600 7926.8980 3951.1492 14227.1680 10745.7290
## 811.3445_7.959 812.545_7.958 1604.1946_7.949 1225.3783_7.964 1606.2002_7.968
## 2 2689.3547 4522.8545 5575.0215 8367.9040 4865.0280
## 1620.6864_7.972 820.5825_7.912 1215.3898_7.986 1580.196_7.986 1581.2003_7.986
## 2 7794.1480 7906.4290 8153.8105 8360.7840 8650.4210
## 1214.8875_7.988 1536.1354_7.989 1619.1791_7.991 1537.1392_7.989
## 2 9454.2780 7501.6694 25151.9940 7450.5186
## 1214.3846_7.993 771.6088_7.993 1618.1753_7.994 770.6056_7.993 1646.2057_7.997
## 2 9955.2040 21877.8140 19924.5230 45203.0550 9875.2490
## 1608.188_7.997 1597.1987_7.999 1607.6828_7.999 1596.195_7.999 1203.3894_8.009
## 2 9616.7790 238179.7200 8933.7910 237418.1000 8534.0230
## 1647.2105_8.001 1597.2743_8.003 1213.88_8.002 1607.1867_8.003 811.6598_8.009
## 2 9716.3730 10932.1640 8690.0570 9580.7330 15617.2100
## 1606.677_8.009 1202.8865_8.011 1556.1974_8.013 1557.2015_8.015
## 2 7183.5640 10947.6090 7585.3350 8709.0260
## 1625.2149_8.037 810.6564_8.03 1595.6827_8.019 1547.1806_8.02 1191.3906_8.062
## 2 12292.9170 27826.2540 14310.0230 6703.2856 15374.7880
## 1546.1772_8.02 918.7347_8.057 788.6651_8.049 1576.2067_8.049 788.4624_8.049
## 2 7400.9910 26834.8260 28974.5920 47163.5900 5721.8413
## 1202.3827_8.089 787.4429_8.052 787.1747_8.049 901.7361_8.046 788.4791_8.049
## 2 15047.3470 8742.4740 5992.7470 3997.7410 6112.3823
## 787.1568_8.05 1573.2005_8.05 787.0639_8.049 787.6075_8.051 787.4152_8.05
## 2 7667.6406 693332.7000 5572.2020 2972381.8000 7381.6720
## 787.4054_8.05 789.0553_8.052 788.4066_8.049 787.4705_8.05 887.7206_8.05
## 2 10284.9510 7108.6410 5121.1380 9619.8320 17676.8000
## 789.6103_8.05 787.2199_8.049 788.5214_8.05 788.6624_8.05 787.246_8.049
## 2 137969.9400 7135.8613 7705.8003 28974.5920 5294.0020
## 788.3967_8.051 786.6043_8.051 787.2651_8.051 787.4549_8.049 788.3339_8.051
## 2 4785.5156 5057976.0000 7128.3306 7922.1340 4397.5060
## 787.2508_8.05 1623.2097_7.994 787.2891_8.052 788.4473_8.05 789.0076_8.052
## 2 7128.3306 67560.0100 9301.7000 5244.8296 7598.0586
## 1574.2787_8.051 787.3251_8.05 790.6125_8.05 787.3567_8.05 787.1311_8.049
## 2 19970.3090 8355.4610 24993.3570 8801.4720 4919.2285
## 788.3678_8.051 787.0988_8.052 787.394_8.051 788.5317_8.052 788.5585_8.05
## 2 4608.7680 4709.1510 6810.9150 7705.8003 8609.7760
## 1572.1972_8.051 788.4344_8.049 1624.213_8.033 787.2834_8.051 787.1808_8.051
## 2 713128.7000 5311.0120 31627.4410 6348.5605 5992.7470
## 787.2998_8.051 787.0264_8.052 787.5336_8.052 787.3787_8.051 873.705_8.052
## 2 9301.7000 5334.6816 16162.1090 8637.3880 21801.8070
## 787.4344_8.052 786.998_8.053 789.039_8.054 786.5592_8.053 836.6167_8.053
## 2 8742.4740 5534.2007 8507.6920 13340.0500 164887.2800
## 837.6197_8.054 788.2955_8.051 917.7314_8.057 1574.2746_8.051 1625.2172_8.052
## 2 92194.1600 4771.1570 41920.6450 19970.3090 12292.9170
## 788.4171_8.052 787.5853_8.055 1577.208_8.052 1583.684_8.06 1190.8891_8.06
## 2 5784.9043 11014.6820 12456.6830 18803.3830 19758.0530
## 1584.1871_8.061 1504.14_8.066 809.6525_8.065 1542.1805_8.073 798.0928_8.066
## 2 21277.4650 6772.1220 35540.1330 1776.1273 17759.5550
## 1580.1886_8.021 903.7157_8.069 797.5914_8.07 1512.1341_8.076 1513.1387_8.078
## 2 7128.2500 54982.0400 20372.8700 4902.0320 4631.7810
## 1594.1759_8.08 1596.1936_8.011 1595.1798_8.081 1015.8403_8.077
## 2 37922.5080 237418.1000 43863.7500 4834.1772
## 1534.1768_8.083 1578.1482_8.087 1579.1512_8.088 1531.2124_8.09
## 2 2277.1152 8681.5790 7062.4985 2929.3164
## 1538.1459_8.043 1560.1917_8.095 1561.1945_8.097 1201.8801_8.097
## 2 4576.0415 6615.8640 6840.0800 19030.6540
## 748.5844_8.099 1601.1358_8.098 792.5534_8.102 838.6248_8.065 858.5976_8.109
## 2 7808.4106 2936.8900 20987.0800 27926.5350 9422.0000
## 1505.1673_8.109 844.6529_8.109 1568.1908_8.106 1504.1651_8.11 824.5559_8.11
## 2 4884.8370 9276.4620 2051.2266 6692.3066 11680.5940
## 861.6684_8.111 808.5829_8.112 745.6214_8.115 814.5355_8.122 876.5697_8.13
## 2 8620.0170 225450.6600 33230.9300 4392.0170 14577.0130
## 797.6166_8.131 812.615_8.136 782.6048_8.136 797.622_8.139 813.6188_8.138
## 2 13172.6850 55408.0800 13588.5570 13172.6850 30250.1230
## 718.5742_8.138 796.6199_8.138 740.5559_8.156 804.5876_8.154 803.6626_8.177
## 2 35830.4900 27612.8930 7921.2920 3303.4630 3644.0823
## 738.543_8.181 831.6353_8.196 794.6054_8.214 816.5873_8.216 772.5244_8.234
## 2 3907.0308 7501.3530 17375.9900 5054.9526 6606.4736
## 748.5258_8.233 750.5444_8.236 726.5431_8.239 726.5428_8.237 796.5824_8.278
## 2 5351.6120 14193.0200 10719.5840 10719.5840 15188.2330
## 742.5718_8.28 774.6005_8.288 720.5897_8.289 840.5869_8.291 818.6044_8.299
## 2 10703.9160 62710.8050 45988.0700 2605.9014 11294.8580
## 812.518_8.311 1559.0847_8.317 790.5354_8.318 1536.1002_8.319 869.6735_8.319
## 2 11549.1290 3963.9385 45920.6330 5456.7140 8030.8490
## 855.6577_8.319 1558.0818_8.319 768.554_8.322 766.5713_8.323 1537.103_8.32
## 2 10435.9850 4421.0864 137888.3600 9783.3010 6037.8916
## 861.7043_8.325 1512.129_8.321 744.5895_8.327 859.53_8.35 1603.1463_8.354
## 2 3211.8820 1559.1877 44567.2500 5301.6504 4844.4280
## 1602.1428_8.354 1581.1647_8.356 771.6366_8.357 577.5185_8.358 1580.16_8.36
## 2 4260.9030 6483.5720 19273.0550 9046.0080 6051.5376
## 837.5477_8.363 854.574_8.362 844.6193_8.384 1556.1852_8.372 796.621_8.383
## 2 6388.8820 5900.6200 2343.3713 2861.2085 42591.8100
## 1598.2072_8.389 1599.2108_8.39 797.6421_8.39 1609.235_8.392 870.6724_8.39
## 2 2189.4500 2292.6287 22040.9400 2434.6880 8020.4740
## 798.6518_8.391 768.5859_8.446 1608.2268_8.392 834.5987_8.394 913.7362_8.395
## 2 9237.9250 16053.6450 2900.1672 153612.4200 7233.6950
## 850.5715_8.394 1240.9027_8.395 1647.2081_8.395 1636.2128_8.395
## 2 7632.6410 5042.2656 5742.0024 1433.1611
## 1646.2051_8.395 1241.4043_8.395 870.6699_8.393 823.6064_8.398 943.7468_8.396
## 2 4859.8794 3468.6155 8020.4740 7488.8115 16223.1800
## 1625.2283_8.396 899.7208_8.396 812.6186_8.396 902.5848_8.397 1624.2248_8.397
## 2 28604.9120 6616.0740 1047427.9400 11303.6440 23280.4040
## 829.6782_8.401 1229.9108_8.397 929.7313_8.398 887.6842_8.398 928.5901_8.4
## 2 2905.9430 1786.8480 30229.9940 9576.3630 3768.2612
## 1488.1014_8.407 1579.148_8.401 1556.1645_8.4 812.5816_8.403 1578.1446_8.401
## 2 1599.0760 6521.3916 8926.7280 4463.5210 5768.2610
## 744.5536_8.408 1558.2047_8.408 831.6573_8.408 836.6141_8.438 766.5356_8.411
## 2 67014.3900 5426.7380 6645.1600 67513.0000 17174.8070
## 1559.2145_8.411 1648.2179_8.411 835.6663_8.407 1649.2233_8.415 797.6453_8.401
## 2 4953.9040 4564.8623 3489.7559 4294.2360 18578.6950
## 800.616_8.426 812.5434_8.427 746.6055_8.435 836.6148_8.439 862.6306_8.44
## 2 7890.5930 26353.0020 87903.0400 67513.0000 4183.6226
## 834.5249_8.443 863.7199_8.448 953.7312_8.446 768.587_8.451 858.5977_8.453
## 2 8687.0380 4957.3994 2759.5295 16053.6450 14241.1680
## 838.626_8.451 820.6211_8.471 842.6021_8.473 827.663_8.483 872.6526_8.477
## 2 12044.2730 19120.0120 5486.3965 2012.2316 2843.0916
## 732.589_8.53 822.6349_8.528 816.5872_8.538 911.7203_8.543 1681.1596_8.548
## 2 2401.3503 13368.5080 13415.1080 2542.0745 1467.8942
## 794.6057_8.544 846.6354_8.545 824.616_8.544 627.5345_8.545 904.5911_8.546
## 2 55114.2030 4117.5950 8617.4610 51406.0040 123015.2700
## 887.564_8.547 974.6686_8.55 909.5461_8.55 988.6841_8.551 931.5277_8.561
## 2 59375.2900 16389.7000 76121.2100 12244.3060 18477.8870
## 628.54_8.549 682.5516_8.574 682.5424_8.581 797.6383_8.603 774.5401_8.626
## 2 26479.1230 3619.2190 2558.0005 5545.0244 4538.0350
## 800.6163_8.637 772.6196_8.643 762.6465_8.683 770.6056_8.664 887.7201_8.662
## 2 17393.1390 18083.7420 11254.5070 34141.5270 3043.4792
## 879.715_8.665 762.6007_8.665 762.6003_8.665 792.5873_8.665 784.5827_8.668
## 2 3347.9624 69766.6900 69766.6900 7310.0664 14434.0940
## 783.6251_8.708 760.6411_8.71 761.6436_8.707 603.5342_8.674 885.5458_8.676
## 2 12006.4740 178180.7800 47402.3000 12115.9220 12543.1630
## 880.5906_8.676 881.5938_8.68 863.5639_8.681 863.6636_8.706 798.5401_8.707
## 2 17983.9360 8863.7240 10738.4630 3641.4660 12199.6130
## 776.5588_8.709 864.7169_8.689 782.6223_8.717 759.6382_8.714 781.6192_8.718
## 2 31320.0100 1566.1786 37748.9650 377947.8000 79606.5800
## 913.7359_8.729 818.603_8.731 796.6218_8.731 1592.2329_8.736 1556.2518_8.736
## 2 10978.3260 33741.2660 184381.3600 1825.2245 2584.3364
## 927.7519_8.733 1555.2487_8.735 1581.2641_8.744 1518.2654_8.731 1544.281_8.743
## 2 4942.7180 2985.7680 1573.4025 2188.2922 1780.5391
## 812.6149_8.745 787.6592_8.744 785.6536_8.747 786.6565_8.746 807.6347_8.75
## 2 29369.2540 24286.8890 155052.8800 84624.1950 34807.3670
## 808.6375_8.751 792.551_8.764 770.569_8.759 1096.0441_8.765 1585.2312_8.763
## 2 19958.3960 3824.7146 10167.5220 4770.1170 2009.1298
## 1547.245_8.769 813.6183_8.757 1584.2261_8.767 872.652_8.777 838.6321_8.779
## 2 1813.7694 15552.5370 1495.6147 3547.7883 71823.5000
## 955.7465_8.781 860.6133_8.785 802.5738_8.798 812.6135_8.749 844.6182_8.806
## 2 4056.5420 19085.7480 2034.8574 29369.2540 9798.0690
## 822.6365_8.807 1626.2381_8.815 772.6209_8.839 772.6206_8.839 1560.2277_8.857
## 2 37384.1760 1795.6345 36643.1300 36643.1300 2528.1110
## 895.575_8.852 1610.2416_8.847 1561.2317_8.857 868.5564_8.853 1560.2279_8.857
## 2 9122.6810 1606.6274 2096.7686 8738.1090 2528.1110
## 970.718_8.858 878.5852_8.857 1205.4054_8.863 1193.9116_8.867 1204.9038_8.864
## 2 2072.0452 15522.6260 3425.4846 2413.8950 6387.4320
## 889.7358_8.865 799.6069_8.865 810.5988_8.865 1204.4035_8.865 846.6352_8.854
## 2 6697.9346 6983.0093 162977.5800 4851.0835 3632.3313
## 826.5716_8.868 1599.2099_8.871 788.6177_8.867 875.7207_8.87 919.7465_8.87
## 2 8242.2870 6221.9805 865810.5600 7441.5850 15927.7960
## 1600.2146_8.869 905.7311_8.871 1598.2061_8.871 1576.2251_8.873
## 2 4016.7202 29489.8850 5958.5270 23088.7300
## 1577.2283_8.872 863.6838_8.874 846.6653_8.88 846.6641_8.878 920.75_8.87
## 2 23334.6720 7788.0283 6441.1836 6441.1836 8393.0180
## 862.6249_8.847 798.635_8.894 1562.15_8.919 1541.1702_8.921 1540.1672_8.92
## 2 2585.2780 6228.3520 2620.2407 3824.1492 4577.3970
## 1574.2638_8.923 1573.2611_8.925 1599.2772_8.928 1600.2791_8.929
## 2 6542.3990 6040.3880 5463.1610 5027.1150
## 752.5592_8.969 1540.1695_8.922 634.54_8.941 1424.1384_8.941 835.6566_8.968
## 2 121846.2340 4577.3970 11649.0200 1106.2727 16823.7230
## 807.6349_8.948 1570.2972_8.954 1571.3005_8.952 785.6542_8.953 1618.2939_8.956
## 2 100125.7600 4251.0780 3762.8994 522479.3000 2913.5898
## 1619.2973_8.957 1597.3162_8.956 1226.4736_8.96 1596.3127_8.957 902.7673_8.957
## 2 2636.0408 6456.7925 2384.7297 7183.5527 4785.4880
## 864.6463_8.959 1622.3273_8.96 1623.3314_8.96 1538.2067_8.962 811.6703_8.961
## 2 3853.0010 3328.8547 2920.3967 8198.0080 452180.2000
## 1537.2035_8.961 942.7986_8.961 928.7831_8.964 1563.2185_8.966 1564.2222_8.965
## 2 8158.6310 3112.7622 4864.0244 6779.1787 7188.4610
## 1559.1849_8.966 820.6364_8.966 1560.1891_8.966 1505.1127_8.971 752.5594_8.973
## 2 4831.3480 5543.6120 4781.2480 3282.3630 121846.2340
## 833.6507_8.971 1504.1092_8.971 1585.2008_8.971 1586.2043_8.972 839.6622_8.973
## 2 90670.3750 3353.8713 3973.0388 4660.1130 11185.8020
## 1526.0908_8.976 1527.094_8.977 853.6786_8.975 853.6786_8.974 774.5404_8.982
## 2 2261.3677 2447.8135 6491.6400 6065.3050 33070.9960
## 901.6373_8.994 796.5222_9.007 773.6516_9.025 848.6521_9.028 870.6336_9.039
## 2 5242.9307 6539.7510 5768.3096 10455.5700 3922.0080
## 778.5756_9.056 800.5558_9.058 639.4955_9.064 634.5399_9.061 814.6319_9.087
## 2 21589.3960 9973.2360 9008.7020 17308.4400 45744.3240
## 728.5588_9.081 772.5221_9.085 815.6606_9.084 814.6318_9.087 836.6136_9.086
## 2 36172.3630 4829.6094 6896.4450 43821.0350 12368.0340
## 815.6352_9.088 976.684_9.115 889.5795_9.114 889.5796_9.114 906.6063_9.114
## 2 23855.8630 3964.2112 10294.8530 10294.8530 19006.6900
## 911.5614_9.119 907.6093_9.114 761.6526_9.142 860.6131_9.152 838.6301_9.147
## 2 15033.8630 9790.6840 13240.4760 3334.6848 10393.5520
## 761.6524_9.144 783.6349_9.156 824.6527_9.167 798.6374_9.202 787.6674_9.202
## 2 14064.4000 4326.3877 3069.5030 10325.2540 11397.3890
## 837.6831_9.217 746.5685_9.239 836.6135_9.214 1134.7862_9.281 805.6784_9.291
## 2 2259.9702 23541.2090 5999.7593 2264.9692 3160.8816
## 831.694_9.318 799.6686_9.32 821.6501_9.321 853.6758_9.321 825.6815_9.335
## 2 9682.3840 64151.5080 19314.0740 4403.1426 4570.6104
## 795.6345_9.346 773.6531_9.348 796.6374_9.346 829.6788_9.393 790.5588_9.389
## 2 31471.4530 106267.2400 15768.1130 2430.6433 29043.0450
## 812.5409_9.393 835.6652_9.434 813.6834_9.437 911.5609_9.505 748.6211_9.504
## 2 14324.1460 2862.2705 5873.9604 3171.0100 8226.1540
## 906.6086_9.509 830.6471_9.551 825.6817_9.564 825.682_9.561 825.6778_9.575
## 2 4015.0657 2937.1077 5497.7520 5497.7520 5497.7520
## 799.6692_9.585 821.6503_9.585 889.6375_9.586 824.6537_9.596 865.5795_9.626
## 2 126674.4500 40410.2420 5655.4565 8026.0327 9856.3400
## 882.6062_9.627 605.5497_9.629 887.5614_9.632 610.5401_9.655 874.6674_9.727
## 2 11044.4110 12343.1300 13329.2830 28221.0230 3739.4768
## 946.718_9.767 822.6412_9.776 994.7158_9.773 972.7339_9.773 610.5401_9.784
## 2 4369.6255 3652.0278 3394.0657 6745.5063 26610.0080
## 575.5027_9.782 775.6661_9.814 848.6521_9.817 802.5715_9.823 780.578_9.823
## 2 7375.3936 3828.1284 6023.0610 7545.5100 20116.2090
## 837.6724_9.906 636.5558_9.845 877.6374_9.86 1454.1868_9.88 1428.1716_9.868
## 2 21972.9940 42947.8500 7760.8450 2822.0042 3884.6777
## 790.6308_9.873 836.6696_9.91 809.6507_9.876 825.6241_9.886 1229.9993_9.9
## 2 10953.7200 68698.6640 142217.1200 5103.1763 5663.7266
## 1216.4896_9.892 1596.3093_9.895 822.6567_9.894 904.7835_9.894 787.6701_9.896
## 2 6419.0737 2667.2332 9440.6710 7669.8687 764947.4000
## 918.7987_9.897 1575.3325_9.895 1597.3131_9.894 1216.9911_9.896
## 2 3990.7144 9844.8850 4571.2026 6257.8867
## 1574.3292_9.897 1610.32_9.892 1218.0011_9.9 1205.4975_9.898 1601.3486_9.899
## 2 9586.8050 2037.4537 2942.3462 2605.5000 15603.9630
## 1624.8418_9.901 1612.335_9.9 1600.3452_9.899 1622.3263_9.899 1204.9948_9.897
## 2 1690.1177 3212.6940 17856.8520 6902.9717 2218.2852
## 1623.3297_9.9 1626.36_9.901 1229.4977_9.901 823.1597_9.895 1636.3352_9.901
## 2 6103.9395 7419.6340 5963.9860 9982.1730 1853.3842
## 1218.5051_9.902 813.6866_9.902 1611.8348_9.901 851.64_9.902 1648.3413_9.902
## 2 2684.0940 690934.4400 1809.0951 6419.0205 2774.8425
## 944.8145_9.903 1635.8345_9.901 930.7989_9.903 1627.364_9.902 1231.5117_9.905
## 2 4155.0170 2170.7930 6697.0640 7962.1280 1438.7896
## 1649.3448_9.904 1242.5053_9.906 835.6666_9.91 1637.3363_9.913 1624.3343_9.899
## 2 2797.6785 2477.2144 129321.3500 1596.2676 4526.2295
## 903.6531_9.918 1454.1867_9.904 1455.1905_9.914 811.657_9.886 900.6836_9.96
## 2 7390.5300 2822.0042 2837.3228 22864.7850 3004.9622
## 824.6526_9.963 816.6467_9.969 846.6342_9.978 850.668_9.982 1260.0333_9.997
## 2 22027.6130 9653.9910 6442.0044 21643.2930 1689.8410
## 636.5558_9.995 877.6374_9.988 872.6492_9.995 641.5114_9.997 874.6655_10.042
## 2 56788.6400 7760.8450 6610.9883 25429.3830 4552.4736
## 1285.8106_10.085 1237.8134_10.084 1259.7951_10.087 1263.8289_10.086
## 2 3408.2200 6988.3945 5009.2383 4262.3896
## 848.6549_10.11 839.6989_10.178 802.5716_10.204 893.6243_10.204
## 2 3186.0747 6096.6980 5188.3584 5797.2056
## 903.6531_10.207 780.5795_10.219 835.6664_10.22 1242.5051_10.22
## 2 8449.8540 22486.0720 103869.7700 2082.8200
## 813.6854_10.226 1649.3444_10.225 1626.3599_10.225 1627.3632_10.227
## 2 381365.5300 2159.2188 3409.6960 2932.7860
## 930.7988_10.228 993.7984_10.238 876.6835_10.245 850.6799_10.276
## 2 3466.2046 1542.3507 22272.3380 10435.3720
## 827.6998_10.277 849.6809_10.279 790.6874_10.339 837.6789_10.324
## 2 60154.3360 14998.0840 22034.1110 9877.1330
## 789.6837_10.343 817.7051_10.344 1263.8289_10.349 789.6834_10.343
## 2 40494.2800 6994.6670 5731.1290 40494.2800
## 1136.8021_10.373 784.6654_10.38 810.6812_10.387 827.6999_10.389
## 2 3619.1929 49122.8320 25851.0210 26534.7050
## 1392.1715_10.401 823.6664_10.409 918.7994_10.407 1624.3414_10.412
## 2 3190.6000 62043.8160 4696.2188 2813.8708
## 801.686_10.409 1602.3605_10.41 1603.364_10.411 612.5558_10.456 577.5187_10.46
## 2 692194.6000 11764.9390 12013.2330 77427.2500 32375.0840
## 1251.8293_10.466 576.5712_10.467 1273.811_10.468 638.5716_10.5
## 2 8640.0060 5095.9053 3408.7230 165529.3800
## 827.6999_10.502 828.702_10.497 603.5343_10.505 1061.8543_10.506 974.75_10.504
## 2 25615.8340 15173.9660 34431.8750 3343.0020 11808.7040
## 1281.8397_10.519 620.5974_10.522 602.5849_10.522 646.6126_10.526
## 2 6460.2583 18829.7050 5623.0010 11654.9940
## 798.681_10.534 876.6836_10.537 837.6818_10.567 1631.3955_10.568
## 2 32928.6000 16258.8250 39000.6450 28180.2300
## 1630.3921_10.568 815.7025_10.568 932.8152_10.571 1693.3899_10.581
## 2 28998.0080 1358551.4000 14281.7510 3901.8900
## 1694.3938_10.581 1265.8449_10.582 1266.8482_10.583 878.6996_10.587
## 2 3288.1887 12453.4190 8237.1250 66347.1600
## 852.6843_10.599 812.6971_10.626 634.6121_10.629 794.6868_10.627
## 2 28785.6070 79744.4140 13066.2030 8050.0093
## 829.7155_10.657 604.6026_10.678 622.6132_10.675 648.6287_10.678
## 2 93435.5860 24810.2850 92957.0600 65970.8050
## 630.6177_10.682 750.7353_10.695 766.6703_10.657 636.6288_10.754
## 2 16101.6500 13713.2750 2498.5890 61143.0350
## 618.6182_10.756 1298.1983_10.827 1297.1947_10.828 1301.284_10.831
## 2 18534.6330 14894.1600 15831.9220 9458.5210
## 650.6449_10.83 632.6339_10.83 1300.2808_10.831 1323.2657_10.832
## 2 231979.9800 61316.7150 10455.5960 7166.7656
## 1322.2624_10.832 737.7496_10.83 751.765_10.833 652.6596_10.839
## 2 7450.3690 99716.0400 43819.7660 29194.0640
## 698.6194_10.879 369.3508_10.879 822.6568_10.855 706.612_10.9 367.3357_10.902
## 2 7585.7500 7600.8240 1118.7153 503.6229 2734.6660
## 1312.1372_10.911 536.437_10.971 537.4409_10.971 794.7051_10.958
## 2 1260.3538 15194.1230 6173.0380 1532.0643
## 764.6932_10.985 750.6771_10.977 723.7681_10.986 990.7559_11.04
## 2 3286.6401 4052.7825 6541.2540 503.6229
## 916.7378_11.078 986.8154_11.075 966.7541_11.082 790.6914_11.103
## 2 4055.8555 2141.5740 1308.7278 14667.0980
## 871.6776_11.111 866.723_11.11 897.6938_11.125 1012.8318_11.122
## 2 1971.7200 11646.0570 2771.5180 2667.5410
## 942.7538_11.123 816.7069_11.125 886.7839_11.127 992.7701_11.13
## 2 7002.9956 24550.1150 8966.7450 1220.1979
## 892.7383_11.126 947.7089_11.125 1026.8485_11.128 893.7418_11.129
## 2 13834.8020 1734.7812 1478.2411 9333.2820
## 847.6774_11.153 842.723_11.155 912.7986_11.157 1077.8922_11.168
## 2 4512.6826 35983.4730 12624.6630 3036.9690
## 1052.864_11.17 923.7092_11.174 988.8322_11.176 918.754_11.178 873.694_11.187
## 2 1462.3789 4076.4150 5921.2407 24098.1270 7523.4478
## 873.6937_11.187 1038.8471_11.18 868.7389_11.19 938.8159_11.193 537.4408_11.2
## 2 7523.4478 1416.3619 59879.3550 20733.0400 10269.4940
## 536.437_11.201 973.7251_11.18 899.7091_11.203 964.8324_11.205 978.8481_11.206
## 2 22663.3340 1255.9167 9732.2420 18701.9000 8140.4380
## 894.7548_11.21 895.7581_11.209 949.7248_11.221 792.7074_11.221
## 2 68870.3500 46552.7540 3564.0085 53668.0040
## 1028.8632_11.223 1014.8477_11.222 944.7698_11.221 994.7855_11.222
## 2 2733.2075 5224.7300 21662.3790 1416.0491
## 965.8354_11.205 1064.8634_11.211 823.6785_11.238 818.7234_11.241
## 2 14816.6560 1068.8082 12250.7220 122100.3100
## 888.8006_11.24 979.8509_11.21 880.718_11.248 1651.3848_11.251 966.8463_11.305
## 2 42924.8700 7065.9873 25625.6050 1242.2499 78760.4450
## 1676.3968_11.262 1677.4008_11.264 849.6939_11.262 958.844_11.264
## 2 1796.0378 1726.6401 20410.2400 4713.4663
## 972.8574_11.264 844.7397_11.265 914.817_11.265 928.8321_11.265 984.86_11.28
## 2 2451.9100 204800.8000 66042.9300 29528.2870 4351.0660
## 970.786_11.284 875.7095_11.292 870.7558_11.296 940.8331_11.297
## 2 7937.1480 30375.4340 324633.8400 108197.8600
## 925.7249_11.296 954.8481_11.299 996.8_11.288 990.8484_11.304 920.7705_11.302
## 2 10130.7440 44881.9300 1424.0710 21417.8360 85782.5700
## 967.8518_11.315 1004.8642_11.302 896.7714_11.316 980.8638_11.316
## 2 52803.0660 8446.4310 248298.4800 32281.8000
## 980.8635_11.316 966.8486_11.316 896.7712_11.316 901.7251_11.318
## 2 32281.8000 78760.4450 248298.4800 24962.9080
## 832.7384_11.319 1030.8788_11.336 1016.8631_11.335 946.7855_11.335
## 2 20185.0740 5249.5107 12976.7540 45298.5660
## 951.7402_11.339 863.7092_11.343 858.7545_11.345 972.8_11.345 1678.4082_11.291
## 2 5979.0874 4331.8610 38934.5350 6095.9890 1496.6875
## 897.7743_11.318 794.7232_11.353 864.8008_11.354 1603.3857_11.359
## 2 159883.8400 83499.6600 35786.2300 1324.0599
## 1602.3814_11.358 884.7697_11.358 1629.4012_11.367 1628.3971_11.365
## 2 1567.7642 22661.8000 2671.4620 3046.2450
## 820.7395_11.368 942.849_11.418 927.7399_11.374 1654.4129_11.376
## 2 301490.2000 478812.5600 12192.0160 4399.8080
## 1655.4167_11.377 934.7878_11.376 992.8634_11.384 851.7097_11.384
## 2 4814.8080 3561.1330 46830.5660 37883.4060
## 968.863_11.436 1006.8788_11.385 1680.4284_11.386 922.7862_11.386
## 2 199250.5200 19874.9860 8051.8286 174604.8400
## 1681.4323_11.387 846.7559_11.39 930.848_11.39 916.8333_11.39 872.7731_11.418
## 2 7821.1704 682281.0600 114990.0900 257165.2500 1616521.8000
## 877.7253_11.418 956.8644_11.419 942.8496_11.419 872.7324_11.418 924.8_11.503
## 2 60751.1130 203498.6000 478812.5600 9127.0020 217531.5300
## 898.7872_11.44 982.8793_11.44 968.8643_11.442 971.877_11.497 903.7406_11.457
## 2 669157.7000 92480.0700 199250.5200 17941.2070 37241.9920
## 834.7543_11.459 1018.8786_11.465 948.8005_11.465 1032.894_11.465
## 2 47546.9900 10522.3040 41912.8870 4827.4473
## 953.7558_11.474 974.8146_11.474 860.7704_11.477 865.7253_11.477
## 2 3773.4314 6077.1630 140002.2000 11221.6790
## 936.8015_11.483 891.7406_11.489 886.7858_11.492 929.7558_11.499
## 2 4311.1226 8177.3180 89998.2700 14997.7480
## 994.8787_11.507 1020.8931_11.508 1008.8943_11.508 1034.9093_11.511
## 2 61630.4180 7597.8490 25257.6680 3466.2903
## 1633.4321_11.51 924.8016_11.511 950.8156_11.512 1628.4765_11.515
## 2 2025.4548 228336.0500 27785.3790 1713.1843
## 1658.4443_11.517 892.8326_11.516 1659.4478_11.517 822.755_11.516
## 2 6331.3030 145534.3100 5374.6123 287093.4000
## 1653.489_11.522 1654.4923_11.523 1684.4596_11.522 1685.4637_11.522
## 2 3217.0054 3502.0134 7813.4136 11191.2860
## 951.819_11.515 848.7717_11.527 1679.5046_11.53 1680.5083_11.531
## 2 20169.0200 967240.2000 6822.7740 7704.7427
## 876.6825_11.558 877.7965_11.559 944.8656_11.558 958.8804_11.56
## 2 3346.4666 122302.6640 890314.0000 398300.1600
## 874.7892_11.559 874.7461_11.559 875.5569_11.56 876.854_11.56 820.6961_11.574
## 2 3172819.5000 15439.8930 2351.3545 21837.2010 4369.5210
## 879.7408_11.578 912.8727_11.58 688.6024_11.581 900.803_11.585 970.8801_11.587
## 2 48040.1050 6471.0737 21189.0570 1112878.0000 325268.1000
## 984.8952_11.587 905.7564_11.605 950.8167_11.564 1020.8946_11.578
## 2 149427.5600 44519.5300 27785.3790 7168.3706
## 976.8319_11.618 927.8194_11.647 862.7859_11.623 1034.911_11.594
## 2 3982.1274 77872.8200 222026.7300 2552.7888
## 955.7715_11.624 996.8931_11.636 1010.9091_11.638 888.8014_11.642
## 2 1778.2245 34972.3000 17250.7500 190564.5000
## 1416.1569_11.643 714.6184_11.643 1417.1607_11.643 893.7563_11.645
## 2 1047.7401 102431.1640 1195.3859 23155.9860
## 931.7715_11.646 926.8159_11.647 719.5732_11.65 1022.9086_11.654
## 2 15659.1980 114237.9450 6053.1020 8046.4526
## 1036.925_11.654 1366.1408_11.655 957.7865_11.655 1367.1442_11.655
## 2 4327.8200 2090.9631 4914.2324 1783.4105
## 952.831_11.66 978.8456_11.66 1316.1245_11.664 1317.1277_11.664
## 2 27333.9430 2786.0613 2743.7852 3071.8890
## 664.6011_11.668 1311.1692_11.669 1312.1724_11.668 928.8306_11.753
## 2 34742.4400 3528.9402 2835.1895 67667.6640
## 1689.4913_11.681 1688.4878_11.681 1657.5142_11.686 1683.5318_11.692
## 2 5808.5100 4370.8804 503.6229 5529.8125
## 1684.5352_11.693 960.8944_11.707 946.8797_11.708 876.8035_11.712
## 2 6383.3037 428250.1600 899821.7500 3226125.5000
## 879.8107_11.711 902.8168_11.73 876.7617_11.713 878.587_11.712 972.8948_11.737
## 2 120675.1250 895668.6000 17487.1330 2670.4592 269066.0600
## 902.8182_11.738 986.9104_11.739 1554.3397_11.745 1555.3432_11.744
## 2 1224358.8000 127751.4900 4530.2593 3650.8150
## 1041.9353_11.754 1363.2025_11.753 1364.2059_11.753 690.6188_11.754
## 2 2514.9795 24606.1390 25608.9410 252108.4400
## 673.5905_11.756 1369.1611_11.766 1368.1577_11.767 907.7722_11.767
## 2 8385.0060 16062.5750 14618.8570 60527.9300
## 928.8317_11.769 695.5729_11.772 1012.925_11.773 998.9084_11.775
## 2 67667.6640 14446.9390 9726.1090 22197.7090
## 933.7875_11.787 1339.2017_11.786 1344.1575_11.789 1345.161_11.79
## 2 11010.6820 3396.5154 8898.4680 7879.2250
## 895.7721_11.798 890.8172_11.801 1268.1254_11.807 1295.1453_11.81
## 2 39354.1840 222009.8100 4658.1943 10834.8230
## 1294.1419_11.81 1263.1707_11.815 991.9207_11.815 1289.1866_11.82
## 2 11917.8960 3417.1736 4366.9340 12816.2420
## 1290.1901_11.819 1320.1579_11.832 1321.1613_11.836 369.3517_11.844
## 2 14974.6510 39232.8050 39623.7000 182142.1200
## 1322.1646_11.831 666.619_11.852 370.3549_11.851 1298.1748_11.858
## 2 24688.4920 524568.4400 54212.8670 6319.6870
## 1299.1785_11.856 1017.9359_11.856 1318.2126_11.857 1316.2069_11.859
## 2 6578.4690 25785.3770 28259.0700 165839.2000
## 1315.2035_11.858 1341.2177_11.895 1342.2211_11.896 692.6335_11.899
## 2 159781.9500 7678.6963 8293.4130 46673.7700
## 693.6368_11.899 1532.3557_11.908 1346.1728_11.908 1347.1765_11.909
## 2 23759.4550 3606.4485 6072.4260 6827.9200
## 904.8336_11.91 1558.3711_11.912 988.9264_11.914 974.911_11.915
## 2 709680.4400 4606.4585 99856.6250 219563.4200
## 930.8479_11.919 1000.9255_11.924 1014.9413_11.924 1334.1724_11.947
## 2 77632.0200 26061.6200 12150.8350 1344.1984
## 956.8628_11.946 1308.157_11.948 1322.172_12.024 1350.2033_12.028
## 2 8415.5060 1872.2688 4146.2305 2119.1028
## 1324.1888_12.056 1325.1924_12.055 1299.1762_12.078 370.3549_12.075
## 2 27354.7800 28560.8160 15242.0160 53898.5500
## 1019.9516_12.075 1319.2346_12.075 1303.2104_12.075 1302.2067_12.075
## 2 54721.5500 125490.6000 15517.7540 16427.4260
## 1320.238_12.076 668.6342_12.074 1277.1948_12.078 369.3518_12.077
## 2 123558.3700 166722.5600 12821.5625 186192.3300
## 1294.2217_12.077 1268.2053_12.079 1293.2183_12.078 1326.1956_12.056
## 2 50350.7700 4661.6890 53581.9500 15722.6530
## 1267.2019_12.079 1276.1913_12.079 1250.1754_12.08 642.6107_12.077
## 2 5505.1060 13411.2990 4338.9240 13245.4710
## 1298.1727_12.089 1272.1559_12.098 1028.9572_12.137 963.8345_12.135
## 2 15940.2180 2504.3152 7405.4510 6037.5570
## 984.8939_12.149 1329.2228_12.335 1328.2189_12.336
## 2 4043.2595 2327.4583 2329.1943
# change to results to numeric
data_notame <- data_notame %>%
mutate_at(-1, as.numeric)
head(data_notame, n = 1)## mz_rt 189.1598_0.513 104.1071_0.537 184.0946_0.54
## 2 x5109_b1_beta_c18pos_65 5393.717 173277.3 51759
## 162.1126_0.545 204.1231_0.589 265.2403_0.591 144.102_0.595 118.0864_0.597
## 2 144957.5 35302.47 25206.29 496116.2 213544.2
## 337.0619_0.611 132.102_0.612 623.1355_0.614 116.0707_0.623 645.1222_0.627
## 2 54898.49 54694.32 11556.97 33922.21 12947.25
## 293.0984_0.628 100.0757_0.629 160.0606_0.629 353.0342_0.629 172.1333_0.62
## 2 669172.9 35459.49 98662.82 26301.62 39135.14
## 120.0808_0.631 166.0861_0.629 258.1105_0.632 310.2012_0.633 188.0707_0.633
## 2 42466.13 49830.12 8127.888 22907.95 38425.38
## 629.1522_0.633 308.1855_0.633 286.2013_0.633 331.0535_0.634 167.0891_0.631
## 2 27773.81 1071.162 37006.47 115179.9 6466.679
## 247.1441_0.636 315.0805_0.637 256.1695_0.637 277.1263_0.644 332.243_0.64
## 2 19227.89 835308.9 503.6229 1397.252 6094.11
## 398.3413_0.639 585.2705_0.643 660.0815_0.643 265.1183_0.643 272.1651_0.645
## 2 503.6229 106325 6281.265 25677.9 1143.596
## 235.0925_0.646 152.0706_0.642 682.0633_0.646 207.1109_0.641 287.1003_0.648
## 2 21110.93 503.6229 4690.715 503.6229 14763.28
## 314.2326_0.654 464.1914_0.656 293.1101_0.657 338.065_0.618 377.2467_0.663
## 2 98041.81 2922.865 13778.01 9382.85 14033.57
## 328.2481_0.675 634.4366_0.676 316.2483_0.679 316.2608_0.679 340.2481_0.684
## 2 18997.65 3395.837 172184.2 10606.57 8409.402
## 648.4519_0.687 692.4782_0.686 736.5043_0.684 533.3253_0.69 706.4934_0.689
## 2 5077.35 4088.035 2706.916 503.6229 3497.064
## 330.2637_0.689 515.3654_0.688 588.4113_0.688 368.2792_0.689 750.52_0.687
## 2 9743.125 3182.81 1957.799 10114.12 2877.078
## 342.2638_0.69 508.3578_0.689 346.0093_0.691 240.1585_0.692 643.38_0.69
## 2 34116.93 3075.123 25780.42 503.6229 1043.223
## 356.2794_0.691 363.2166_0.691 254.1387_0.695 344.2792_0.693 205.1221_0.621
## 2 5844.374 7206.663 503.6229 7690.018 5093.727
## 259.0944_0.697 616.1763_0.702 425.1649_0.679 423.1686_0.697 429.24_0.73
## 2 503.6229 4483.476 503.6229 503.6229 1073.373
## 346.0093_0.919 616.1763_0.902 383.1533_0.931 344.2793_0.927 466.3162_1.004
## 2 22806.4 2304.904 503.6229 8477.92 503.6229
## 167.0854_1.05 308.1256_1.117 286.1437_1.13 373.2483_1.147 353.079_1.243
## 2 503.6229 1220.951 9559.344 2644.51 503.6229
## 337.1048_1.268 315.1224_1.279 585.2701_1.361 450.3212_1.383 426.3213_1.384
## 2 1094.075 503.6229 16384.21 4375.302 1517.117
## 413.2797_1.415 399.2641_1.416 312.1597_1.413 463.2323_1.708 531.2196_1.712
## 2 1245.323 1691.521 1938.883 1366.684 1879.289
## 526.2644_1.714 424.3418_1.822 163.0753_1.983 365.1356_1.988 343.1537_2.002
## 2 2830.343 12478.95 1823.27 1293.21 1224.583
## 430.2589_1.992 400.3418_2.122 542.3237_2.234 426.3575_2.274 518.3238_2.281
## 2 503.6229 20248.24 15779.09 25978.1 10080.8
## 468.3082_2.293 516.3057_2.488 494.3238_2.494 590.3212_2.604 568.3394_2.605
## 2 36834.36 6834.443 41801.2 7841.76 42264.55
## 445.2944_2.63 566.3214_2.69 536.2833_2.749 544.34_2.697 482.3238_2.701
## 2 2599.151 56812.7 2144.942 368163.5 43307.03
## 1087.6686_2.703 1085.6532_2.71 1063.67_2.714 1001.6557_2.715 526.2925_2.719
## 2 8532.202 11860.9 45158.13 5241.096 19726.09
## 1128.671_2.722 1045.6241_2.726 578.3923_2.734 522.3458_2.735 1039.6724_2.735
## 2 3874.508 4286.007 8663.166 104104.3 141075.4
## 1050.6573_2.737 1580.9848_2.737 637.4547_2.737 520.3416_2.737 854.4963_2.738
## 2 3425.48 2591.293 9267.76 2110536 1008.148
## 1061.6537_2.738 558.2953_2.739 801.9871_2.739 1051.1585_2.738 802.4888_2.74
## 2 37508.34 25146.85 6660.126 4586.067 5628.042
## 1077.6264_2.732 542.3219_2.742 1321.82_2.739 428.3732_2.759 603.2923_2.783
## 2 2352.143 216674.8 1340.773 22848.51 3954.632
## 524.2743_2.81 502.2925_2.81 570.3547_2.836 500.2744_2.854 478.2926_2.855
## 2 9483.88 45643.72 18142.38 12592.04 59690.58
## 508.3393_2.879 568.337_2.996 546.3554_2.996 1041.6857_3.056 1041.6842_3.059
## 2 8472.406 19313.49 96251.14 7538.01 7538.01
## 518.322_3.062 534.2952_3.067 766.4889_3.074 765.9874_3.073 554.3925_3.077
## 2 261189.1 29635.33 12595.91 14793.69 14094.73
## 515.313_3.077 762.9789_3.079 1002.658_3.079 1029.627_3.079 1003.1594_3.079
## 2 6508.718 3456.11 12248.12 7750.699 12764.03
## 1018.1281_3.082 1250.8236_3.081 1013.6543_3.08 1011.1465_3.08 1258.8122_3.08
## 2 3244.351 3285.566 134102.8 4763.765 4368.292
## 1010.6446_3.081 1508.9859_3.081 1261.821_3.081 1018.6298_3.081 496.6809_3.081
## 2 3859.514 23519.38 12869.56 4046.246 4457.254
## 1509.9892_3.081 583.4441_3.082 597.4596_3.082 498.346_3.082 627.4702_3.082
## 2 21969.56 12773 8819.312 195381.3 10046.37
## 1487.0039_3.082 991.6743_3.082 496.342_3.082 613.4547_3.084 519.3249_3.064
## 2 15511.97 879353.2 4294420 17320.18 74408.87
## 1026.6606_3.098 1339.8437_3.087 1017.6873_3.095 1039.6688_3.106
## 2 3632.041 503.6229 57205.26 12549.96
## 844.5121_3.103 454.2925_3.202 560.3108_3.237 522.3564_3.24 639.47_3.238
## 2 1817.037 27750.66 17206.49 1083036 5109.145
## 1043.703_3.238 544.3374_3.238 1065.6846_3.239 805.0108_3.238 572.3709_3.254
## 2 42585.41 173100.4 13565.86 3742.731 11761.46
## 502.2902_3.355 480.3082_3.357 480.3446_3.381 482.3601_3.403 504.3422_3.406
## 2 12590.91 50793.16 30530.71 30982.69 6784.817
## 548.3709_3.408 510.3551_3.445 438.2976_3.525 508.3757_3.535 808.0344_3.778
## 2 13561.27 46895.31 13126.19 19657.02 9954.235
## 582.4238_3.778 808.536_3.778 1059.2224_3.779 1069.7165_3.778 1571.0972_3.779
## 2 6841.065 8679.472 2830.685 38131.31 1797.102
## 524.373_3.779 562.3265_3.778 1593.0791_3.779 1047.7355_3.778 1331.8987_3.778
## 2 2038640 20471.24 3281.99 137818.2 2004.737
## 611.4752_3.779 641.486_3.779 526.3772_3.778 1012.7169_3.78 1594.0825_3.778
## 2 7494.673 14114.69 105510.5 3057.31 3136.277
## 546.3534_3.779 614.3398_3.784 550.3862_3.859 482.3239_3.884 504.3061_3.884
## 2 276992.4 11940.78 14665.81 47507.31 17674.91
## 585.2706_3.939 1191.515_3.94 607.2524_3.94 299.139_3.94 686.3908_3.94
## 2 146034.2 16599.96 52378.4 24249.07 6995.175
## 672.3751_3.941 1169.5332_3.94 1213.4964_3.94 629.2342_3.942 508.3754_4.012
## 2 18887.17 25512.06 6557.705 15115.13 6429.108
## 510.3914_4.038 538.3859_4.051 664.5997_4.075 466.3289_4.123 541.3858_4.202
## 2 14236.21 5810.855 503.6229 17835.53 2574.608
## 561.4122_4.23 521.4196_4.236 539.4303_4.244 563.4268_4.262 317.2685_4.264
## 2 23843.74 33456.54 10956.25 12931.34 7061.09
## 552.4018_4.301 593.4768_4.312 563.4273_4.415 619.4811_4.462 798.5661_4.516
## 2 10377.73 5784.829 18468 3092.176 503.6229
## 593.4757_4.54 712.5953_4.577 569.4277_4.639 568.4266_4.64 543.4017_4.622
## 2 4907.904 1515.657 5953.48 12313.46 4421.932
## 617.4748_4.643 593.4762_4.644 595.4929_4.645 577.4817_4.655 515.3703_4.774
## 2 41282.35 5630.025 18788.27 6552.479 1753.653
## 774.5634_4.81 822.5634_4.827 596.4154_4.906 620.4935_4.951 317.2685_4.946
## 2 1473.547 503.6229 2267.32 8821.808 13718.4
## 641.4722_4.95 619.4903_4.953 647.5118_4.967 669.4937_4.965 800.5792_5.005
## 2 4980.564 20838.72 16498.73 3707.525 1091.9
## 798.5655_5.067 695.5093_5.094 673.5278_5.092 669.5575_5.14 610.431_5.16
## 2 1007.246 8457.689 31376.89 2030.23 4867.011
## 597.5086_5.242 822.563_5.243 798.564_5.251 592.4686_5.28 683.5094_5.286
## 2 4058.869 503.6229 503.6229 4339.037 3237.377
## 661.5282_5.288 719.5696_5.337 802.5954_5.397 752.5219_5.513 608.4637_5.649
## 2 11441.75 3824.059 503.6229 2898.786 4542.179
## 1372.06_5.66 834.5932_5.658 765.5124_5.66 1350.0784_5.66 697.5255_5.661
## 2 1504.125 7477.324 10079.44 2835.277 106392.8
## 675.5442_5.66 713.5011_5.661 755.4833_5.662 1035.2944_5.66 834.5933_5.658
## 2 386197.2 3942.706 6335.622 2901.371 8029.474
## 754.5378_5.732 678.5059_5.698 826.5945_5.722 828.5522_5.728 860.609_5.81
## 2 3039.894 5521.155 503.6229 1888.697 6816.476
## 791.5279_5.817 1424.0918_5.818 739.5144_5.819 723.5413_5.819 1074.3179_5.819
## 2 10553.44 3385.897 5583.948 144145.3 3523.327
## 1402.1101_5.819 1403.1135_5.82 1425.0951_5.82 701.5604_5.818 1073.8159_5.82
## 2 4824.275 5107.778 2573.156 599996.9 2832.278
## 1227.7565_5.834 804.5524_5.837 826.5347_5.837 1249.7383_5.838 778.5376_5.842
## 2 12203.02 12628 3870.69 8835.819 12066.24
## 800.5194_5.843 614.3823_5.849 780.5532_5.895 781.5567_5.896 894.5999_5.902
## 2 2907.38 3563.692 11540.62 6917.907 503.6229
## 796.5458_5.943 774.5637_5.944 518.4564_5.944 532.4721_5.945 699.5412_5.971
## 2 3604.923 13888.01 11549.34 7264.864 3503.865
## 677.5586_5.973 826.5348_5.993 677.5582_5.973 826.594_5.988 776.5195_6.001
## 2 13189.33 2856.227 13189.33 503.6229 11136.42
## 754.5373_6.002 1378.109_6.06 689.5597_6.062 711.5408_6.063 870.596_6.054
## 2 40443.72 1011.074 200887.3 50749.24 1772.125
## 1460.0658_6.066 1419.0884_6.065 721.5835_6.066 749.5557_6.066 727.5745_6.067
## 2 503.6229 1109.91 9074.263 5750.457 19276.98
## 847.6527_6.068 721.5846_6.067 730.5383_6.068 752.5201_6.07 768.4931_6.068
## 2 2905.475 9074.263 114104.4 27510.76 1770.284
## 854.5686_6.074 719.5692_6.151 741.551_6.153 1024.6773_6.157 1046.6591_6.159
## 2 6152.89 15576.9 4452.868 10911.22 6503.981
## 836.4936_6.219 846.5226_6.22 873.6685_6.266 778.5354_6.223 756.5541_6.24
## 2 4037.495 4128.233 2879.385 25760.33 110201.8
## 852.5506_6.226 830.568_6.228 852.5508_6.228 715.5745_6.252 814.535_6.26
## 2 5595.419 18959.27 5595.419 15226.4 3462.799
## 792.5531_6.261 1282.9156_6.311 860.4936_6.313 870.5225_6.318 1192.8093_6.322
## 2 12696.33 1923.939 6466.28 9003.215 2011.703
## 887.5125_6.318 803.037_6.321 855.6216_6.322 838.606_6.327 802.5357_6.323
## 2 6353.087 3670.396 3051.434 1869.002 91769.53
## 867.6575_6.328 897.6684_6.323 1583.0843_6.325 1582.0806_6.324 818.5091_6.325
## 2 2372.927 11467.24 1582.399 1568.461 4040.898
## 1332.9345_6.33 1181.3124_6.332 780.5549_6.325 1560.0983_6.326 1572.0922_6.339
## 2 503.6229 1025.953 368834.2 4451.918 503.6229
## 1561.1021_6.326 911.6839_6.326 1083.7892_6.33 791.5397_6.411 1536.0989_6.339
## 2 4407.13 6154.823 2970.02 6074.74 2921.405
## 1180.809_6.327 1586.1131_6.353 1537.1023_6.339 815.5406_6.313 1558.081_6.341
## 2 1127.522 3110.242 2659.378 3513.512 503.6229
## 856.5843_6.351 1584.092_6.345 808.5757_6.369 1563.1161_6.369 806.5698_6.37
## 2 4378.549 1198.424 29549.92 2475.094 195438.1
## 937.6995_6.371 807.5729_6.37 1612.1288_6.374 1613.1329_6.372 1562.1109_6.352
## 2 2721.302 102868.9 1763.787 1275.695 3185.352
## 896.5379_6.373 828.5511_6.373 756.5542_6.373 923.684_6.374 1513.1028_6.383
## 2 4877.083 45829.29 125456.4 6563.71 1131.322
## 778.5355_6.376 873.6684_6.375 1512.0991_6.376 887.6837_6.371 783.5732_6.445
## 2 29409.03 4166.919 503.6229 1988.407 331912.1
## 691.5742_6.403 1586.1128_6.4 862.5091_6.42 1587.1171_6.406 790.5352_6.424
## 2 3770.091 4523.083 5067.804 4852.676 10887.09
## 872.5379_6.428 804.5511_6.435 768.5536_6.434 857.6371_6.438 1195.8324_6.435
## 2 8783.236 111106.5 55822.8 5338.684 2865.332
## 820.5246_6.44 899.684_6.44 869.6734_6.444 1551.1176_6.447 765.0535_6.446
## 2 6667.659 17594.67 3180.421 3467.197 6213.062
## 913.6995_6.445 1155.8333_6.446 1156.3359_6.446 1588.1268_6.447
## 2 8412.68 3080.686 4607.505 6853.002
## 1588.1294_6.449 1550.1138_6.449 782.5712_6.447 1536.6212_6.45 840.6219_6.452
## 2 6853.002 4199.678 678390 1190.592 6660.055
## 884.6064_6.452 1589.1333_6.45 1538.1147_6.455 1536.1192_6.444 1564.1312_6.455
## 2 9413.58 5112.16 5550.979 1409.711 19175.45
## 1565.1348_6.456 1539.1179_6.455 1496.1206_6.463 1496.6252_6.461
## 2 18705.8 4691.44 2144.165 2699.307
## 1116.8381_6.462 1497.1271_6.467 1508.122_6.465 1507.1178_6.465
## 2 8629.759 2977.185 10895.97 13234.06
## 1472.1218_6.472 862.6247_6.471 1468.623_6.472 1468.1206_6.471 1471.118_6.473
## 2 3205.907 29764.36 5167.526 5868.002 5601.302
## 1509.1328_6.475 1457.6299_6.479 1486.1407_6.477 1457.1286_6.478
## 2 11411.89 4680.134 61512.78 5138.557
## 1485.1374_6.478 1527.117_6.481 1565.1864_6.477 1526.1136_6.481 1510.1375_6.48
## 2 60516.32 6122.256 4547.977 6177.338 9231.854
## 1483.1167_6.498 704.4038_6.503 704.3938_6.501 704.5181_6.5 706.5843_6.5
## 2 3701.038 2979.16 3859.773 5754.961 62212.01
## 703.8986_6.501 703.9516_6.503 705.6376_6.502 703.5779_6.502 704.5113_6.501
## 2 4795.362 4313.908 12838.21 3894128 4318.408
## 1469.1118_6.499 1406.1439_6.504 834.7053_6.506 1448.1242_6.506
## 2 6197.371 201903.3 8837.926 23742.54
## 1418.1346_6.507 1447.1208_6.507 820.6896_6.508 1417.6332_6.508 1065.848_6.509
## 2 11429.71 28251.12 14094.65 11612.35 5100.89
## 1066.3498_6.509 1469.1116_6.5 744.5538_6.51 1459.1207_6.512 1419.1345_6.509
## 2 7944.83 6197.371 130015.8 21799.07 4790.94
## 1428.1239_6.513 1429.1272_6.513 1460.1239_6.512 1489.1055_6.52
## 2 38435.32 39882.62 19428.6 3819.562
## 1500.0993_6.518 1512.1013_6.516 758.5603_6.497 1444.1182_6.522
## 2 2863.62 1807.76 16470.03 2425.514
## 1451.0918_6.532 1077.3414_6.528 1411.1131_6.529 757.5569_6.495 1481.1047_6.53
## 2 1944.388 19003.99 4645.933 54360.38 4764.314
## 1482.1063_6.531 1479.1431_6.532 1410.1118_6.532 741.5301_6.535
## 2 3807.777 1477.4 9581.128 13782.2
## 1463.0927_6.535 1413.0725_6.543 1443.1241_6.536 1431.0932_6.55 725.5571_6.544
## 2 1717.434 1060.23 503.6229 2014.533 260720.5
## 706.5381_6.548 1409.1106_6.553 1187.9262_6.553 776.579_6.552 823.6523_6.559
## 2 93600.58 6711.315 4113.951 13343.06 3093.488
## 728.5196_6.564 794.5671_6.58 762.541_6.576 445.3676_6.577 795.5713_6.591
## 2 16527.31 9425.65 1313.938 109687.9 4672.062
## 738.5064_6.603 1175.7015_6.637 1153.7198_6.64 822.6001_6.635 770.5692_6.675
## 2 2870.319 6597.094 11591.68 503.6229 20639.82
## 886.5092_6.697 1576.1291_6.701 751.5715_6.701 1192.8412_6.709 807.3448_6.714
## 2 6770.491 2247.034 44493.56 4918.515 2046.742
## 1193.3431_6.71 1232.3347_6.716 1557.1328_6.71 1585.1303_6.711 1558.1357_6.713
## 2 5770.275 7525.565 4170.914 2856.057 3915.788
## 818.0604_6.711 1458.1721_6.714 1585.6318_6.711 729.5912_6.714 1635.1157_6.712
## 2 4732.001 2063.349 2523.966 306413.6 17849.02
## 1634.1123_6.713 923.6842_6.713 1536.1554_6.715 1535.1521_6.713 806.5725_6.713
## 2 15352.74 32366.5 17665.03 17672.51 1990779
## 896.5381_6.715 1586.1251_6.718 1597.1201_6.716 893.6733_6.713 1623.6198_6.714
## 2 12253.86 2667.938 2787.466 6461.125 3466.673
## 1596.1232_6.714 808.6366_6.716 937.6999_6.715 1612.1323_6.715 907.689_6.715
## 2 2560.059 11384.07 19629.97 77630.02 4811.047
## 1624.1217_6.715 1613.1357_6.716 1596.6247_6.715 1231.8327_6.718
## 2 5639.593 80207.57 2719.564 8712.688
## 1540.1229_6.721 1597.613_6.72 1539.1194_6.72 1538.1166_6.72 829.5547_6.726
## 2 18157.97 3647.161 30808.2 31225.44 112573.4
## 1462.1391_6.719 806.973_6.716 1624.6226_6.715 1461.136_6.721 1598.1117_6.724
## 2 3168.688 1876.863 3109.778 3381.061 3605.303
## 844.5247_6.723 881.6374_6.724 1465.1023_6.726 1194.3346_6.723 1561.1011_6.726
## 2 12924.33 8552.931 3474.738 4337.534 5940.24
## 732.5542_6.723 1560.0999_6.725 828.5517_6.726 1464.0995_6.726 863.6839_6.733
## 2 276055.4 6536.032 217082.3 3964.644 3554.819
## 1486.0818_6.746 830.5605_6.803 1588.1294_6.745 819.6577_6.728 810.5875_6.727
## 2 1056.584 36147.89 6560.44 2858.344 16855.02
## 864.6223_6.744 791.5433_6.743 849.6682_6.746 1570.1174_6.785 1571.1203_6.766
## 2 9567.526 4449.957 8039.013 2437.093 1673.151
## 1232.8362_6.721 1564.133_6.867 752.574_6.803 1639.1441_6.788 764.5588_6.812
## 2 6162.027 1592.23 22218.88 1738.531 19950.18
## 1389.0311_6.813 446.3707_6.814 829.5548_6.746 848.6162_6.816 1547.1209_6.83
## 2 2245.297 15048.38 112573.4 1549.012 1851.035
## 445.3675_6.816 786.5403_6.817 1610.1103_6.827 816.5509_6.821 794.5677_6.822
## 2 44383.05 13006.08 2360.677 12866.78 23135.77
## 770.5691_6.825 756.5471_6.823 752.5739_6.808 754.5356_6.825 830.5623_6.824
## 2 45051.02 13398.25 21862.45 82551.06 44440.26
## 775.6384_6.825 1638.1373_6.816 751.5717_6.812 889.5277_6.826 792.5512_6.827
## 2 9095.53 2424.075 41401.25 12069.74 31182.04
## 1189.3305_6.84 862.5095_6.827 429.3727_6.826 812.5558_6.826 740.5662_6.833
## 2 2290.845 12648.29 29734.88 9023.723 11398.99
## 717.5903_6.828 827.6071_6.849 718.5932_6.828 725.5564_6.829 788.554_6.828
## 2 31804.75 6890.672 18248.29 6774.083 7176.18
## 824.5758_6.83 1233.3393_6.794 703.5745_6.822 1546.1205_6.851 806.6235_6.835
## 2 5052.685 2624.717 17905.92 2267.558 8408.094
## 1664.1589_6.841 1341.0272_6.83 1536.1083_6.846 802.5963_6.831 790.5738_6.832
## 2 503.6229 1807.081 4108.062 5939.143 14675.88
## 791.5771_6.835 870.5402_6.834 854.5671_6.833 762.5403_6.835 742.5356_6.834
## 2 10014.84 5618.031 95410.94 5182.002 11383.88
## 1363.015_6.835 742.5728_6.833 1365.0287_6.836 800.5726_6.827 793.556_6.829
## 2 4216.765 6285.115 6167.119 2866.086 19775.47
## 1339.0138_6.829 820.5836_6.837 755.6054_6.838 720.5531_6.843 949.6995_6.841
## 2 1964.223 3550.497 12663.03 20646 3175.722
## 1207.3311_6.841 832.5855_6.839 740.5626_6.841 780.5519_6.84 963.7151_6.844
## 2 3592.237 215492.3 11398.99 268613.8 2147.896
## 805.617_6.84 1170.3294_6.844 740.5609_6.842 727.5724_6.844 1169.3422_6.843
## 2 23781.82 5499.694 11398.99 71935.27 2549.335
## 1208.3368_6.844 1221.342_6.847 796.5247_6.843 1208.8392_6.844 804.553_6.847
## 2 8168.149 8990.758 13196.66 8508.403 1490167
## 1220.8404_6.847 1171.3299_6.847 822.528_6.849 1490.1152_6.846 706.5938_6.852
## 2 11420 6874.279 12749.64 2711.425 67258.38
## 1637.1286_6.848 820.5251_6.85 1537.126_6.857 1550.1223_6.858 705.5909_6.851
## 2 4997.227 56154.94 5891.694 4848.113 164496.8
## 1611.1156_6.852 1144.8407_6.848 1599.1159_6.853 1511.145_6.854 1601.625_6.853
## 2 4880.624 2717.055 4693.414 3669.107 4794.322
## 1549.6231_6.854 1157.3434_6.852 1145.3428_6.85 1171.8322_6.85 1510.1351_6.854
## 2 3179.157 6450.307 2907.835 9182.755 3332.92
## 1599.6188_6.853 766.5727_6.851 1183.8334_6.853 1509.1326_6.853
## 2 5672.694 17519.13 34739.1 3473.422
## 1600.1221_6.854 1184.336_6.855 1611.6196_6.855 1612.6219_6.855
## 2 7925.387 26521.11 4443.62 3518.422
## 1600.6232_6.854 1580.1025_6.858 1536.6252_6.856 1601.125_6.853
## 2 6881.685 3157.408 3068.867 7206.609
## 1536.1177_6.855 1195.8336_6.857 833.6399_6.878 1157.8451_6.853
## 2 4108.062 68607.18 5561.771 7063.84
## 1573.1314_6.859 1548.6249_6.858 1523.1232_6.86 1537.1276_6.858
## 2 2732.128 4955.48 2464.699 5891.694
## 1589.1303_6.863 826.6051_6.862 1562.1129_6.862 1574.6139_6.862
## 2 19960.34 12937 22721.02 20510.01
## 1581.0989_6.863 1553.1294_6.871 1575.1173_6.863 1587.1165_6.864
## 2 2159.307 5932.734 29707.46 73110.51
## 1586.1131_6.864 1487.1515_6.871 1563.1166_6.865 1575.6183_6.866
## 2 56394.65 11787.87 28489.42 38242.09
## 1592.5935_6.867 1563.6184_6.864 1514.1154_6.867 1576.1212_6.867
## 2 3451.812 17199.99 11699.04 35512.44
## 1548.1292_6.867 1572.126_6.868 1580.5988_6.86 840.6221_6.868 1616.1518_6.868
## 2 6371.311 2966.945 2407.343 19593.6 13179.28
## 1615.1494_6.868 784.5042_6.876 785.0201_6.879 1593.594_6.87 1594.0976_6.869
## 2 24269.12 6912.227 7153.885 3847.872 3865.208
## 1502.1143_6.871 1614.1454_6.868 784.7847_6.875 1564.621_6.871 857.6374_6.871
## 2 4114.43 23931.69 22815.42 15694.01 15725.66
## 784.4182_6.876 784.3687_6.876 783.1226_6.878 783.4174_6.877 1593.0936_6.87
## 2 5728.139 5230.898 5396.507 9845.552 3322.19
## 1515.1185_6.869 913.7001_6.872 1487.1511_6.872 783.1504_6.876 785.5792_6.874
## 2 12058.39 55061.86 11787.87 5308.917 142187.2
## 899.6847_6.873 783.3656_6.875 784.779_6.877 782.9105_6.881 784.6308_6.874
## 2 77207.88 9307.482 22815.42 9752.22 31362
## 783.332_6.875 783.4393_6.874 783.3243_6.874 783.2199_6.876 784.3942_6.875
## 2 7112.816 9775.993 9051.518 6609.113 5407.648
## 1577.1237_6.867 784.4291_6.877 783.1601_6.876 785.0064_6.878 783.1327_6.878
## 2 17270.28 6519.669 5370.077 7101.736 5553.358
## 782.5732_6.875 783.2011_6.878 1488.1552_6.873 783.5024_6.875 783.2594_6.875
## 2 5118052 5738.108 10344.36 16438.4 8841.006
## 783.2926_6.876 783.431_6.876 783.2519_6.875 783.2153_6.875 783.3975_6.877
## 2 8879.737 11415.32 8841.006 6609.113 9822.561
## 783.3843_6.877 784.4472_6.876 794.0606_6.876 783.4057_6.877 785.0389_6.88
## 2 7887.037 7024.986 10706.02 9822.561 7854.313
## 785.049_6.877 783.2313_6.877 784.4911_6.877 786.5814_6.876 782.9653_6.878
## 2 6642.376 7012.737 7622.044 29706.6 6474.723
## 783.067_6.877 784.5306_6.877 783.1442_6.879 783.0991_6.879 784.3802_6.879
## 2 5600.567 7788.103 5385.042 4890.15 5426.719
## 784.3306_6.878 782.9942_6.879 783.3522_6.877 783.1872_6.878 784.3478_6.878
## 2 4889.519 5416.948 7914.3 6745.338 6518.784
## 783.3414_6.876 1562.1131_6.863 784.4086_6.876 782.5279_6.881 784.3757_6.877
## 2 8348.811 22721.02 5975.541 14215.55 5426.719
## 1590.1432_6.882 1549.1336_6.872 784.9793_6.877 1548.1302_6.87 1566.2163_6.888
## 2 22002.96 6424.1 8130.741 6655.745 22692.19
## 1564.1347_6.888 897.7049_6.893 1569.1509_6.94 883.6894_6.894 869.6738_6.894
## 2 850852.2 6647.499 23776.19 17272.91 20351.44
## 1525.1338_6.906 1565.1377_6.892 784.6324_6.878 1592.1507_6.918
## 2 2977.818 861451.7 31362 12488.21
## 1552.1254_6.917 1463.1508_6.915 1172.8405_6.921 1172.3364_6.884
## 2 12711.5 2801.778 10840.85 9370.101
## 1551.1225_6.925 1550.14_6.908 782.0605_6.928 1591.1484_6.916 1551.6195_6.93
## 2 9737.188 4848.113 8147.896 22023.91 11321.2
## 1540.1336_6.931 1542.2142_6.931 1541.137_6.931 1541.2129_6.933
## 2 486983.7 11582.83 465805.6 18545.88
## 1524.1325_6.944 1160.8402_6.939 1539.62_6.944 1160.3363_6.993 890.7034_7.007
## 2 3996.6 12344.9 13607.31 11612.71 24366.79
## 846.6771_7.001 876.6878_7.013 759.4494_6.981 759.2363_6.979 760.4788_6.981
## 2 14237.49 26663.73 13573.45 9096.314 8931.823
## 759.4107_6.98 873.7049_6.978 760.3659_6.98 759.1825_6.981 761.2418_6.982
## 2 12453.77 8745.555 7240.025 8073.894 7632.623
## 761.5791_6.979 758.6931_6.98 760.5104_6.985 759.3448_6.983 758.5734_6.982
## 2 159828.2 151150.5 10110.68 12389.06 5924760
## 759.3553_6.982 759.5018_6.981 759.317_6.981 759.3092_6.981 760.4052_6.983
## 2 11545.61 19481.56 11961.75 11055.11 7653.589
## 759.3828_6.982 760.4925_6.98 759.4259_6.982 759.3339_6.982 1516.1361_6.982
## 2 13261.56 9171.551 12957.39 12457.11 1410985
## 759.2062_6.983 759.391_6.982 758.9593_6.983 759.2797_6.981 760.6287_6.982
## 2 9527.567 13356.74 7871.16 10960.33 37455.3
## 760.421_6.983 759.2558_6.98 760.5281_6.984 761.2551_6.982 1517.1828_6.983
## 2 8018.087 10437.23 8964.443 7059.67 70068.8
## 759.2209_6.98 760.3904_6.983 759.1347_6.985 758.9871_6.983 759.2864_6.986
## 2 8099.955 7451.463 7856.265 7519.873 10960.33
## 759.1729_6.983 759.1221_6.983 759.0903_6.983 759.243_6.983 760.4378_6.981
## 2 8494.09 8485.67 9391.726 9508.17 7236.265
## 859.6895_6.984 761.0316_6.983 759.1094_6.983 760.5329_6.983 759.058_6.985
## 2 23681.34 9723.29 7969.41 9791.593 7774.229
## 758.5284_6.987 759.4041_6.982 760.9991_6.984 760.3462_6.982 760.2985_6.984
## 2 16056.29 13356.74 10946.29 6063.82 5087.155
## 759.2652_6.984 759.0221_6.985 760.7903_6.983 760.2926_6.982 759.1449_6.983
## 2 10437.23 7333.553 25374.46 5774.973 6841.049
## 760.3968_6.983 760.3759_6.985 760.6318_6.982 1518.2168_6.99 748.5833_7.03
## 2 7653.589 7195.236 35895.73 27325.66 1124.02
## 761.0061_6.983 1528.1243_7.002 1148.8422_7.002 1527.6219_7.002 760.5406_6.984
## 2 10946.29 18321.87 18726.27 16791.13 8467.766
## 1475.1518_7.004 845.6738_7.007 1476.1552_7.005 889.7001_7.009 769.5595_7.011
## 2 7617.863 23587.66 8129.001 40159.9 14592.27
## 770.0615_7.009 1564.1269_7.052 718.5934_7.015 717.5903_7.018 875.6845_7.017
## 2 14226.67 9514.498 18629.59 38487.53 49967.14
## 1523.0877_7.024 1478.1161_7.023 1522.0846_7.024 1479.1185_7.024
## 2 11077.44 5792.93 12143.67 4028.012
## 1568.1537_7.025 1567.1512_7.025 1538.1135_7.026 1539.117_7.026
## 2 73506.92 138715.3 38153.05 43548.25
## 1566.1476_7.026 1553.1308_7.035 1159.3314_7.035 764.5222_7.036 720.5532_7.039
## 2 146112.4 8087.822 9756.19 24172.59 14088.04
## 1550.1462_7.021 1552.6273_7.034 1159.8332_7.037 1544.0675_7.037
## 2 3413.481 8553.492 15630.45 4475.843
## 1545.073_7.035 833.637_7.039 1173.8476_7.044 781.62_7.043 1572.101_7.045
## 2 3244.006 7982.129 6445.105 5688.709 2317.23
## 780.5518_7.046 1173.3456_7.052 811.5943_7.054 1184.8408_7.054 809.5892_7.057
## 2 183253.7 5460.866 19779.6 8766.134 291041.8
## 1617.1644_7.055 808.586_7.055 786.504_7.061 1616.1606_7.056 1578.1434_7.057
## 2 11359.09 588403.1 3929.747 11933.23 6161.712
## 792.5893_7.058 858.5992_7.06 939.7152_7.063 848.5388_7.066 1185.344_7.069
## 2 15391.7 2558.981 7418.66 9512.555 6107.932
## 925.6997_7.067 1172.8404_7.074 830.5667_7.072 1197.3456_7.08 1578.1423_7.058
## 2 11909.18 3174.133 70597.19 3549.967 6161.712
## 1593.1638_7.133 1577.1364_7.091 1564.1272_7.069 794.0604_7.102 793.5586_7.102
## 2 20253.26 2604.576 9514.498 7002.137 6752.857
## 1565.1315_7.095 1577.6354_7.106 731.6047_7.112 1545.1554_7.122 1185.8481_7.12
## 2 7596.23 2974.81 5290.383 7436.127 7499.909
## 1186.3509_7.119 1174.8563_7.123 1543.1503_7.123 1542.1466_7.121
## 2 5486.907 3801.644 33650.15 35511.25
## 1566.6417_7.129 1544.153_7.124 1590.143_7.137 1506.089_7.132 1614.1417_7.109
## 2 2229.713 18147.69 14288.61 1726.009 4281.475
## 871.6892_7.141 1592.1596_7.145 1594.1676_7.138 785.5418_7.137 1507.0917_7.135
## 2 8669.539 26156.43 11131.22 2624.599 1453.571
## 1187.8642_7.137 1579.649_7.139 1187.3609_7.137 885.7047_7.143 842.6373_7.146
## 2 3963.236 3174.793 3975.496 5349.646 8487.827
## 1568.1618_7.14 1591.1473_7.14 1580.1526_7.144 834.6001_7.175 784.5879_7.141
## 2 64757.88 15067.01 3506.306 24930 1692172
## 1198.856_7.141 915.7154_7.143 901.6999_7.143 787.5943_7.141 807.5705_7.147
## 2 8771.701 17416.82 30570.23 41582.98 99010.71
## 1569.166_7.14 822.5404_7.148 806.5675_7.192 1574.148_7.172 1575.1514_7.174
## 2 62605.8 10181.6 197554.7 3456.569 2419.836
## 859.6529_7.212 925.6996_7.166 445.3675_7.185 1530.1458_7.188 1531.1493_7.186
## 2 8776.959 8828.329 12413.96 6399.987 4770.68
## 874.5538_7.197 692.5581_7.191 790.5744_7.193 1598.1451_7.197 1554.142_7.183
## 2 12562.07 7282.055 32107.72 1604.776 2480.263
## 831.57_7.208 812.5561_7.198 907.6886_7.2 746.5692_7.204 830.5666_7.214
## 2 28144.86 7984.733 1989.696 95167.72 48927.34
## 768.551_7.206 748.5272_7.208 842.5663_7.213 820.5846_7.213 864.5249_7.21
## 2 16492.49 5183.915 6109.288 24081.39 6938.945
## 827.6266_7.218 1548.0963_7.222 762.5038_7.219 740.5222_7.221 743.6054_7.227
## 2 3752.666 3244.971 13752.56 42976.86 7719.281
## 1546.0812_7.232 1547.0845_7.233 856.5819_7.24 1524.0983_7.244 1525.1027_7.243
## 2 3483.698 3488.121 6638.001 5453.528 4776.934
## 807.5706_7.257 834.6002_7.257 724.5272_7.284 797.5882_7.268 842.638_7.268
## 2 118741.5 39144.84 1910.823 35788.45 10514
## 796.5845_7.27 1542.1462_7.263 1581.1632_7.283 806.5675_7.265 874.5539_7.248
## 2 72564.86 10236.89 12460.12 221178.7 12604.81
## 1566.1444_7.237 859.6529_7.266 822.5405_7.274 807.0682_7.274 1604.1571_7.283
## 2 6496.696 9165.156 13790.86 14134.13 4398.783
## 902.7033_7.285 1198.8563_7.281 1187.8649_7.285 1580.159_7.285 901.6999_7.285
## 2 22156.02 15122.66 8691.572 16503.86 43110.04
## 1590.144_7.285 1579.6508_7.286 1199.3591_7.285 1602.1511_7.303
## 2 25340.23 6353.373 13333.36 3956.888
## 1619.1782_7.285 1187.3625_7.286 1591.1476_7.288 784.5881_7.29 871.6892_7.289
## 2 6223.696 5950.332 27672 2985056 12021.59
## 788.5964_7.29 1569.2427_7.289 1569.1673_7.289 786.6508_7.289 785.452_7.291
## 2 13002.81 7227.282 169802.8 14278.91 3061.687
## 1568.1636_7.29 915.7155_7.29 885.7047_7.291 787.5945_7.291 916.7188_7.29
## 2 183332.1 26279.46 9076.538 69423.93 16018.36
## 784.9268_7.293 1591.6495_7.292 1199.8618_7.293 1602.6436_7.296 1500.102_7.296
## 2 3556.974 7716.316 8550.24 3842.637 8130.538
## 1592.1607_7.297 1501.1056_7.297 1522.0815_7.299 1618.1731_7.3 1603.1486_7.299
## 2 53719.42 6924.185 3485.388 7398.908 6123.091
## 716.5222_7.301 1593.1654_7.298 1210.8558_7.304 1210.3541_7.309
## 2 29371.99 48039.57 6494.122 5208.963
## 1614.1426_7.311 1615.1461_7.313 1616.1569_7.316 1211.3587_7.312
## 2 6480.641 6684.407 6791.084 6283.586
## 808.5857_7.318 795.5728_7.343 1617.1623_7.316 1557.1645_7.322 1556.1613_7.323
## 2 326688.6 13748.49 5146.992 9951.845 12318.78
## 1616.1572_7.316 809.5888_7.318 939.7152_7.328 1601.1673_7.328 1600.1629_7.331
## 2 6791.084 164733.8 4276.156 2296.292 2488.047
## 810.5975_7.409 925.6999_7.337 1577.1677_7.349 816.589_7.342 830.5666_7.346
## 2 71589.98 6891.444 4408.132 9590.603 40156.2
## 1576.1637_7.357 792.5899_7.352 909.7046_7.351 814.5717_7.361 766.5377_7.38
## 2 4759.798 52424.32 3424.445 10382.27 27209.41
## 1550.1483_7.401 1574.1454_7.378 719.6006_7.392 1572.1304_7.394
## 2 4758.88 2446.968 4816.713 1660.778
## 1524.1333_7.404 824.6209_7.408 1551.1527_7.401 1562.1493_7.418
## 2 1292.635 2627.002 5710.29 2471.592
## 1550.1497_7.403 718.5737_7.412 1533.1433_7.416 788.5563_7.415 766.5755_7.417
## 2 4758.88 8682.358 3260.227 57011.77 263405
## 1532.1401_7.417 1562.1488_7.418 1563.1535_7.418 897.7047_7.419 883.6892_7.419
## 2 2746.207 2471.592 2596.255 6101.311 12916.71
## 818.5668_7.42 1554.1324_7.421 796.5848_7.423 1577.1679_7.401 1576.1634_7.391
## 2 22529.65 1357.014 107921 2470.981 3383.889
## 1539.1546_7.459 927.7143_7.45 812.6068_7.457 832.5817_7.451 811.6039_7.456
## 2 2163.313 6838.992 22156.73 31705.1 73096.53
## 810.6006_7.457 764.5195_7.458 742.5377_7.462 1538.1514_7.472 881.5145_7.469
## 2 134224.1 7721.074 22778.2 2572.721 6062.229
## 859.5323_7.471 840.5868_7.472 876.559_7.475 818.6053_7.48 1497.1674_7.486
## 2 5798.906 8956.46 11421.36 37945.43 1581.141
## 807.6359_7.483 750.599_7.485 1583.178_7.489 1582.1737_7.485 1583.1777_7.489
## 2 3831.017 503.6229 2480.384 2310.015 2480.384
## 1508.1468_7.513 794.568_7.494 889.6996_7.495 1541.1852_7.515 772.5855_7.501
## 2 3585.358 38519.91 7052.889 3199.927 226832.2
## 1500.1349_7.508 1509.1474_7.507 753.5881_7.505 1544.1615_7.508
## 2 2126.286 2092.075 85613.7 4649.395
## 1545.1646_7.511 1503.1775_7.514 1504.1822_7.513 822.5994_7.509 731.6073_7.511
## 2 4911.405 6527.674 3927.533 13095.05 454151.3
## 1463.2071_7.511 1462.204_7.513 1515.1602_7.514 1514.1517_7.514
## 2 3124.485 3205.003 4324.024 4498.192
## 1564.1662_7.508 1473.1717_7.517 1474.1752_7.518 859.6891_7.519
## 2 2016.174 4781.798 3421.702 8154.296
## 1507.1493_7.519 1506.1473_7.519 764.5566_7.52 873.7045_7.52 1508.1474_7.516
## 2 6633.252 6062.327 31794.88 4982.985 3585.358
## 1487.1502_7.525 1485.1447_7.523 742.5753_7.523 1518.1461_7.531
## 2 2613.703 2148.859 191131.1 2861.42
## 1484.1411_7.523 1486.1496_7.524 1465.1678_7.525 1466.1708_7.525
## 2 2470.002 1497.945 6654.898 6344.956
## 1477.1393_7.527 1523.1825_7.536 1476.1365_7.528 1553.1686_7.544
## 2 6809.455 2414.234 6057.234 3506.845
## 1526.1519_7.532 1534.154_7.527 1527.1556_7.532 1541.1773_7.535
## 2 4657.574 2007.279 3840.568 3371.827
## 1518.1432_7.534 1468.1317_7.534 1535.1558_7.533 734.5702_7.535
## 2 2861.42 6072.619 2250.025 353520
## 1469.1342_7.535 865.6997_7.537 851.6839_7.537 756.5512_7.537 821.6735_7.538
## 2 5004.616 5414.057 10778.74 54331.42 4345.489
## 1479.137_7.538 1490.1052_7.54 1540.1625_7.542 804.5937_7.544 794.5862_7.544
## 2 2990.767 2127.088 3679.243 3675.05 15818.02
## 1510.1552_7.549 1511.1587_7.551 1482.0893_7.554 1552.1644_7.555
## 2 4226.115 3603.847 2527.381 3953.855
## 835.5327_7.555 1499.1861_7.555 852.559_7.556 792.5899_7.556 1502.1505_7.558
## 2 4495.509 5060.153 7433.632 79604.41 8881.844
## 1503.1576_7.556 1500.1884_7.555 857.5144_7.562 1495.1496_7.564 849.6468_7.572
## 2 8585.501 4000.768 3715.037 2497.357 1906.305
## 1561.1726_7.566 814.5717_7.564 748.5274_7.569 1496.0473_7.572 1537.1733_7.573
## 2 2592.568 13302.22 33318.01 503.6229 6685.33
## 1517.1166_7.575 1516.1139_7.574 1560.169_7.579 1536.1705_7.575 770.509_7.576
## 2 2530.585 2080.433 2282.432 6401.749 9558.02
## 768.5909_7.579 1578.1799_7.626 899.7204_7.586 835.6299_7.591 790.572_7.588
## 2 425446 4914.774 8112.951 3593.34 71291.92
## 885.7047_7.587 1494.1454_7.592 806.5656_7.605 1512.1627_7.554 786.5917_7.604
## 2 18110.2 2098.24 15177.77 2484.35 12606.69
## 1491.1814_7.626 795.6347_7.605 1579.1825_7.633 832.582_7.631 941.7308_7.633
## 2 2028.23 4027.316 5770.362 32799.28 3788.433
## 902.5744_7.633 812.607_7.646 811.6042_7.645 810.6013_7.646 1578.1793_7.636
## 2 4986.1 33542.04 107926.3 204152.2 5323.63
## 850.5541_7.648 1544.1549_7.686 1592.1591_7.656 1181.7509_7.659
## 2 13692.65 23416.95 3815.001 3838.781
## 1187.8641_7.656 818.6355_7.665 1545.1606_7.669 744.5901_7.661 816.587_7.665
## 2 3432.469 8476.735 9823.201 122544.6 23278.81
## 1526.1435_7.632 1528.1659_7.665 1529.1702_7.665 782.5674_7.669
## 2 3219.7 13098.23 9945.977 204366.2
## 1605.1979_7.674 1571.181_7.67 1570.1764_7.672 1570.1773_7.672 911.7203_7.674
## 2 3559.159 25507.28 26085.3 26085.3 8083.822
## 1604.1948_7.675 798.5402_7.677 1163.3585_7.687 1538.1799_7.62 1539.1868_7.674
## 2 4078.128 12759.44 12428.9 3300.314 3109.004
## 1505.1707_7.678 1576.1618_7.678 1504.1669_7.68 878.7033_7.717 925.736_7.685
## 2 20141.23 4844.107 22547.92 25510.83 4708.039
## 1558.1829_7.689 1162.8568_7.691 794.6062_7.691 1558.1819_7.689
## 2 7927.069 16056.36 297145.4 6884.391
## 1588.1988_7.697 1548.6599_7.695 1589.2021_7.7 1543.1477_7.702 1554.1833_7.701
## 2 4674.947 3720.152 5487.492 43559.92 71208.03
## 1571.1809_7.671 1555.1868_7.702 1542.1448_7.703 1546.1714_7.763
## 2 25507.28 70354.03 40527.9 6810.405
## 1543.1481_7.702 1151.8656_7.71 771.5762_7.712 1531.6527_7.711 1151.3639_7.711
## 2 43559.92 17830.46 17433.56 13362.98 11389.1
## 1544.1542_7.691 761.302_7.716 1532.1582_7.713 861.7048_7.713 877.7_7.713
## 2 23416.95 4584.821 12936.25 20699.18 49098.13
## 1542.645_7.698 760.9218_7.716 1510.1192_7.742 762.5251_7.716 761.5191_7.716
## 2 9268.252 6645.419 2446.165 5528.137 10925.86
## 847.6893_7.716 761.3781_7.717 762.6491_7.715 760.9763_7.718 761.4062_7.717
## 2 24082.52 5340.275 22571.04 4674.224 6146.495
## 761.329_7.717 761.0763_7.717 761.4545_7.719 760.5883_7.719 762.5433_7.717
## 2 5142.379 3327.18 6169.562 4489468 5709.717
## 761.4437_7.717 761.1927_7.717 763.5945_7.717 1521.1684_7.718 1520.1651_7.717
## 2 6169.562 4225.203 111291.4 488071.2 529120.6
## 891.7156_7.718 761.3681_7.718 1531.1683_7.726 1535.1253_7.718 1534.1209_7.719
## 2 34318.62 5340.275 6923.904 5424.926 6476.6
## 761.2595_7.716 761.4141_7.718 760.5459_7.719 761.3174_7.717 1583.089_7.72
## 2 4221.082 5045.675 11911.66 4070.718 7703.644
## 1584.0928_7.721 762.5032_7.719 1521.243_7.719 1629.2009_7.725 1628.1971_7.724
## 2 7824.315 5498.319 19060.62 6422.565 6269.854
## 774.5423_7.725 1608.1354_7.743 1568.1599_7.728 1542.1456_7.705
## 2 11221.32 1272.199 5344.125 40527.9
## 1568.6597_7.736 1569.1611_7.737 1569.1618_7.737 1595.1823_7.739
## 2 6495.05 7725.889 7725.889 98361.68
## 1594.1789_7.739 1588.1975_7.7 1188.8722_7.741 1597.1881_7.738 1579.6524_7.748
## 2 107510.6 4674.947 6300.311 22405.04 4470.258
## 1580.1749_7.748 835.655_7.749 834.602_7.751 1669.196_7.752 1668.1927_7.752
## 2 5792.39 11716.94 448794.2 7539.902 7337.15
## 1616.16_7.754 1617.1626_7.754 1199.864_7.757 1518.2006_7.758 1517.1985_7.759
## 2 7962.745 8248.731 6011.038 14714.34 14677.28
## 965.7308_7.761 1540.1703_7.761 1591.2114_7.764 828.6106_7.766 1592.2118_7.764
## 2 4966.207 5146.007 2566.139 6108.895 3137.566
## 818.6356_7.752 819.5737_7.769 951.7153_7.77 1531.181_7.771 1530.1768_7.745
## 2 10548.59 5129.065 8069.892 7283.269 7336.854
## 757.6221_7.776 856.5823_7.778 798.5402_7.73 1484.1047_7.781 1559.1256_7.781
## 2 173236.7 37189.68 12759.44 12958.7 3212.683
## 1558.1201_7.783 1485.108_7.781 1546.1749_7.78 1604.1937_7.739 780.5921_7.79
## 2 3719.182 12755.68 6810.405 2881.03 21050.65
## 1506.0868_7.79 1547.1793_7.789 1507.0899_7.79 1580.1032_7.792 837.614_7.764
## 2 7164.45 6038.432 6378.97 1895.618 20684.03
## 1481.1402_7.792 858.5914_7.792 825.6473_7.81 821.6234_7.811 820.6201_7.811
## 2 1800.823 7071.552 3993.225 12139.44 22466.33
## 902.5743_7.805 724.5277_7.806 1448.0465_7.808 1470.0283_7.811 770.6049_7.812
## 2 5288.838 82317.88 2385.798 1235.015 53227.27
## 825.6476_7.811 802.5717_7.81 746.5091_7.814 811.6292_7.819 770.6048_7.813
## 2 3993.225 3054.885 22823.37 9954.615 53227.27
## 798.6003_7.83 860.6149_7.833 792.5888_7.837 1532.1012_7.882 820.5823_7.852
## 2 34042.7 6221.181 18303.95 2098.229 8507.365
## 783.6368_7.847 805.6187_7.848 745.6202_7.852 818.6049_7.793 806.611_7.876
## 2 31443.32 9091.662 13998.8 8690.408 4951.175
## 876.5695_7.88 878.5742_7.89 756.5904_7.889 748.5844_7.89 772.5245_7.894
## 2 7075.782 5127.966 9227.396 18335.36 9190.083
## 1547.1803_7.856 700.5274_7.902 1486.1173_7.882 826.6326_7.901 820.6202_7.907
## 2 5399.436 21380.6 3084.073 1115.182 24864.75
## 1578.178_7.924 854.6305_7.919 900.5692_7.918 1511.123_7.922 1510.1192_7.922
## 2 2589.248 4165.847 8471.465 4173.432 4908.784
## 1558.1745_7.924 733.6212_7.929 1566.1816_7.932 734.6246_7.927 733.6215_7.929
## 2 3018.691 26322.75 1988.402 13073.23 26322.75
## 1567.1909_7.931 1570.1778_7.936 848.556_7.937 885.6686_7.937 1544.2137_7.94
## 2 2143.826 10944.23 11359.54 7438.577 3019.196
## 832.5829_7.939 1543.2104_7.94 822.5985_7.942 750.5434_7.943 1582.1177_7.942
## 2 190289.3 2826.24 7101.019 42114.63 6270.005
## 1238.3816_7.948 1604.1947_7.949 1592.1862_7.949 1237.8801_7.949
## 2 11928.05 5575.021 3663.058 12679.16
## 811.0052_7.959 1642.1751_7.953 1643.1784_7.953 1605.1986_7.95 1561.1392_7.955
## 2 4200.295 22001.81 26935.08 5968.865 15708.44
## 812.6663_7.96 822.0921_7.956 1227.3893_7.956 1560.1357_7.954 1632.1881_7.956
## 2 20483.73 13487.08 8973.357 17124.43 12940.04
## 1591.1826_7.956 927.7157_7.955 812.5189_7.959 1226.887_7.956 821.5898_7.956
## 2 3305.585 47009.25 3487.065 13389.33 12952.92
## 813.6106_7.959 1225.88_7.956 1226.3841_7.957 941.7313_7.957 1631.6823_7.958
## 2 105242.4 9936.575 11729.91 34206.33 12720.36
## 1622.2784_7.959 897.705_7.958 911.7206_7.958 811.2977_7.959 810.9483_7.96
## 2 8327.534 16258.5 12577.91 2953.238 8446.533
## 810.6041_7.959 1590.1749_7.958 1621.1991_7.959 811.4149_7.961 814.6127_7.96
## 2 3797555 2573.641 291290.3 3379.271 19150.58
## 1620.1954_7.96 811.5374_7.96 811.4537_7.958 1621.2757_7.96 1631.1866_7.959
## 2 288335.2 7926.898 3951.149 14227.17 10745.73
## 811.3445_7.959 812.545_7.958 1604.1946_7.949 1225.3783_7.964 1606.2002_7.968
## 2 2689.355 4522.855 5575.021 8367.904 4865.028
## 1620.6864_7.972 820.5825_7.912 1215.3898_7.986 1580.196_7.986 1581.2003_7.986
## 2 7794.148 7906.429 8153.81 8360.784 8650.421
## 1214.8875_7.988 1536.1354_7.989 1619.1791_7.991 1537.1392_7.989
## 2 9454.278 7501.669 25151.99 7450.519
## 1214.3846_7.993 771.6088_7.993 1618.1753_7.994 770.6056_7.993 1646.2057_7.997
## 2 9955.204 21877.81 19924.52 45203.06 9875.249
## 1608.188_7.997 1597.1987_7.999 1607.6828_7.999 1596.195_7.999 1203.3894_8.009
## 2 9616.779 238179.7 8933.791 237418.1 8534.023
## 1647.2105_8.001 1597.2743_8.003 1213.88_8.002 1607.1867_8.003 811.6598_8.009
## 2 9716.373 10932.16 8690.057 9580.733 15617.21
## 1606.677_8.009 1202.8865_8.011 1556.1974_8.013 1557.2015_8.015
## 2 7183.564 10947.61 7585.335 8709.026
## 1625.2149_8.037 810.6564_8.03 1595.6827_8.019 1547.1806_8.02 1191.3906_8.062
## 2 12292.92 27826.25 14310.02 6703.286 15374.79
## 1546.1772_8.02 918.7347_8.057 788.6651_8.049 1576.2067_8.049 788.4624_8.049
## 2 7400.991 26834.83 28974.59 47163.59 5721.841
## 1202.3827_8.089 787.4429_8.052 787.1747_8.049 901.7361_8.046 788.4791_8.049
## 2 15047.35 8742.474 5992.747 3997.741 6112.382
## 787.1568_8.05 1573.2005_8.05 787.0639_8.049 787.6075_8.051 787.4152_8.05
## 2 7667.641 693332.7 5572.202 2972382 7381.672
## 787.4054_8.05 789.0553_8.052 788.4066_8.049 787.4705_8.05 887.7206_8.05
## 2 10284.95 7108.641 5121.138 9619.832 17676.8
## 789.6103_8.05 787.2199_8.049 788.5214_8.05 788.6624_8.05 787.246_8.049
## 2 137969.9 7135.861 7705.8 28974.59 5294.002
## 788.3967_8.051 786.6043_8.051 787.2651_8.051 787.4549_8.049 788.3339_8.051
## 2 4785.516 5057976 7128.331 7922.134 4397.506
## 787.2508_8.05 1623.2097_7.994 787.2891_8.052 788.4473_8.05 789.0076_8.052
## 2 7128.331 67560.01 9301.7 5244.83 7598.059
## 1574.2787_8.051 787.3251_8.05 790.6125_8.05 787.3567_8.05 787.1311_8.049
## 2 19970.31 8355.461 24993.36 8801.472 4919.229
## 788.3678_8.051 787.0988_8.052 787.394_8.051 788.5317_8.052 788.5585_8.05
## 2 4608.768 4709.151 6810.915 7705.8 8609.776
## 1572.1972_8.051 788.4344_8.049 1624.213_8.033 787.2834_8.051 787.1808_8.051
## 2 713128.7 5311.012 31627.44 6348.56 5992.747
## 787.2998_8.051 787.0264_8.052 787.5336_8.052 787.3787_8.051 873.705_8.052
## 2 9301.7 5334.682 16162.11 8637.388 21801.81
## 787.4344_8.052 786.998_8.053 789.039_8.054 786.5592_8.053 836.6167_8.053
## 2 8742.474 5534.201 8507.692 13340.05 164887.3
## 837.6197_8.054 788.2955_8.051 917.7314_8.057 1574.2746_8.051 1625.2172_8.052
## 2 92194.16 4771.157 41920.64 19970.31 12292.92
## 788.4171_8.052 787.5853_8.055 1577.208_8.052 1583.684_8.06 1190.8891_8.06
## 2 5784.904 11014.68 12456.68 18803.38 19758.05
## 1584.1871_8.061 1504.14_8.066 809.6525_8.065 1542.1805_8.073 798.0928_8.066
## 2 21277.47 6772.122 35540.13 1776.127 17759.56
## 1580.1886_8.021 903.7157_8.069 797.5914_8.07 1512.1341_8.076 1513.1387_8.078
## 2 7128.25 54982.04 20372.87 4902.032 4631.781
## 1594.1759_8.08 1596.1936_8.011 1595.1798_8.081 1015.8403_8.077
## 2 37922.51 237418.1 43863.75 4834.177
## 1534.1768_8.083 1578.1482_8.087 1579.1512_8.088 1531.2124_8.09
## 2 2277.115 8681.579 7062.498 2929.316
## 1538.1459_8.043 1560.1917_8.095 1561.1945_8.097 1201.8801_8.097
## 2 4576.042 6615.864 6840.08 19030.65
## 748.5844_8.099 1601.1358_8.098 792.5534_8.102 838.6248_8.065 858.5976_8.109
## 2 7808.411 2936.89 20987.08 27926.53 9422
## 1505.1673_8.109 844.6529_8.109 1568.1908_8.106 1504.1651_8.11 824.5559_8.11
## 2 4884.837 9276.462 2051.227 6692.307 11680.59
## 861.6684_8.111 808.5829_8.112 745.6214_8.115 814.5355_8.122 876.5697_8.13
## 2 8620.017 225450.7 33230.93 4392.017 14577.01
## 797.6166_8.131 812.615_8.136 782.6048_8.136 797.622_8.139 813.6188_8.138
## 2 13172.68 55408.08 13588.56 13172.68 30250.12
## 718.5742_8.138 796.6199_8.138 740.5559_8.156 804.5876_8.154 803.6626_8.177
## 2 35830.49 27612.89 7921.292 3303.463 3644.082
## 738.543_8.181 831.6353_8.196 794.6054_8.214 816.5873_8.216 772.5244_8.234
## 2 3907.031 7501.353 17375.99 5054.953 6606.474
## 748.5258_8.233 750.5444_8.236 726.5431_8.239 726.5428_8.237 796.5824_8.278
## 2 5351.612 14193.02 10719.58 10719.58 15188.23
## 742.5718_8.28 774.6005_8.288 720.5897_8.289 840.5869_8.291 818.6044_8.299
## 2 10703.92 62710.81 45988.07 2605.901 11294.86
## 812.518_8.311 1559.0847_8.317 790.5354_8.318 1536.1002_8.319 869.6735_8.319
## 2 11549.13 3963.939 45920.63 5456.714 8030.849
## 855.6577_8.319 1558.0818_8.319 768.554_8.322 766.5713_8.323 1537.103_8.32
## 2 10435.99 4421.086 137888.4 9783.301 6037.892
## 861.7043_8.325 1512.129_8.321 744.5895_8.327 859.53_8.35 1603.1463_8.354
## 2 3211.882 1559.188 44567.25 5301.65 4844.428
## 1602.1428_8.354 1581.1647_8.356 771.6366_8.357 577.5185_8.358 1580.16_8.36
## 2 4260.903 6483.572 19273.06 9046.008 6051.538
## 837.5477_8.363 854.574_8.362 844.6193_8.384 1556.1852_8.372 796.621_8.383
## 2 6388.882 5900.62 2343.371 2861.209 42591.81
## 1598.2072_8.389 1599.2108_8.39 797.6421_8.39 1609.235_8.392 870.6724_8.39
## 2 2189.45 2292.629 22040.94 2434.688 8020.474
## 798.6518_8.391 768.5859_8.446 1608.2268_8.392 834.5987_8.394 913.7362_8.395
## 2 9237.925 16053.65 2900.167 153612.4 7233.695
## 850.5715_8.394 1240.9027_8.395 1647.2081_8.395 1636.2128_8.395
## 2 7632.641 5042.266 5742.002 1433.161
## 1646.2051_8.395 1241.4043_8.395 870.6699_8.393 823.6064_8.398 943.7468_8.396
## 2 4859.879 3468.615 8020.474 7488.811 16223.18
## 1625.2283_8.396 899.7208_8.396 812.6186_8.396 902.5848_8.397 1624.2248_8.397
## 2 28604.91 6616.074 1047428 11303.64 23280.4
## 829.6782_8.401 1229.9108_8.397 929.7313_8.398 887.6842_8.398 928.5901_8.4
## 2 2905.943 1786.848 30229.99 9576.363 3768.261
## 1488.1014_8.407 1579.148_8.401 1556.1645_8.4 812.5816_8.403 1578.1446_8.401
## 2 1599.076 6521.392 8926.728 4463.521 5768.261
## 744.5536_8.408 1558.2047_8.408 831.6573_8.408 836.6141_8.438 766.5356_8.411
## 2 67014.39 5426.738 6645.16 67513 17174.81
## 1559.2145_8.411 1648.2179_8.411 835.6663_8.407 1649.2233_8.415 797.6453_8.401
## 2 4953.904 4564.862 3489.756 4294.236 18578.69
## 800.616_8.426 812.5434_8.427 746.6055_8.435 836.6148_8.439 862.6306_8.44
## 2 7890.593 26353 87903.04 67513 4183.623
## 834.5249_8.443 863.7199_8.448 953.7312_8.446 768.587_8.451 858.5977_8.453
## 2 8687.038 4957.399 2759.53 16053.65 14241.17
## 838.626_8.451 820.6211_8.471 842.6021_8.473 827.663_8.483 872.6526_8.477
## 2 12044.27 19120.01 5486.396 2012.232 2843.092
## 732.589_8.53 822.6349_8.528 816.5872_8.538 911.7203_8.543 1681.1596_8.548
## 2 2401.35 13368.51 13415.11 2542.075 1467.894
## 794.6057_8.544 846.6354_8.545 824.616_8.544 627.5345_8.545 904.5911_8.546
## 2 55114.2 4117.595 8617.461 51406 123015.3
## 887.564_8.547 974.6686_8.55 909.5461_8.55 988.6841_8.551 931.5277_8.561
## 2 59375.29 16389.7 76121.21 12244.31 18477.89
## 628.54_8.549 682.5516_8.574 682.5424_8.581 797.6383_8.603 774.5401_8.626
## 2 26479.12 3619.219 2558.001 5545.024 4538.035
## 800.6163_8.637 772.6196_8.643 762.6465_8.683 770.6056_8.664 887.7201_8.662
## 2 17393.14 18083.74 11254.51 34141.53 3043.479
## 879.715_8.665 762.6007_8.665 762.6003_8.665 792.5873_8.665 784.5827_8.668
## 2 3347.962 69766.69 69766.69 7310.066 14434.09
## 783.6251_8.708 760.6411_8.71 761.6436_8.707 603.5342_8.674 885.5458_8.676
## 2 12006.47 178180.8 47402.3 12115.92 12543.16
## 880.5906_8.676 881.5938_8.68 863.5639_8.681 863.6636_8.706 798.5401_8.707
## 2 17983.94 8863.724 10738.46 3641.466 12199.61
## 776.5588_8.709 864.7169_8.689 782.6223_8.717 759.6382_8.714 781.6192_8.718
## 2 31320.01 1566.179 37748.96 377947.8 79606.58
## 913.7359_8.729 818.603_8.731 796.6218_8.731 1592.2329_8.736 1556.2518_8.736
## 2 10978.33 33741.27 184381.4 1825.225 2584.336
## 927.7519_8.733 1555.2487_8.735 1581.2641_8.744 1518.2654_8.731 1544.281_8.743
## 2 4942.718 2985.768 1573.402 2188.292 1780.539
## 812.6149_8.745 787.6592_8.744 785.6536_8.747 786.6565_8.746 807.6347_8.75
## 2 29369.25 24286.89 155052.9 84624.2 34807.37
## 808.6375_8.751 792.551_8.764 770.569_8.759 1096.0441_8.765 1585.2312_8.763
## 2 19958.4 3824.715 10167.52 4770.117 2009.13
## 1547.245_8.769 813.6183_8.757 1584.2261_8.767 872.652_8.777 838.6321_8.779
## 2 1813.769 15552.54 1495.615 3547.788 71823.5
## 955.7465_8.781 860.6133_8.785 802.5738_8.798 812.6135_8.749 844.6182_8.806
## 2 4056.542 19085.75 2034.857 29369.25 9798.069
## 822.6365_8.807 1626.2381_8.815 772.6209_8.839 772.6206_8.839 1560.2277_8.857
## 2 37384.18 1795.634 36643.13 36643.13 2528.111
## 895.575_8.852 1610.2416_8.847 1561.2317_8.857 868.5564_8.853 1560.2279_8.857
## 2 9122.681 1606.627 2096.769 8738.109 2528.111
## 970.718_8.858 878.5852_8.857 1205.4054_8.863 1193.9116_8.867 1204.9038_8.864
## 2 2072.045 15522.63 3425.485 2413.895 6387.432
## 889.7358_8.865 799.6069_8.865 810.5988_8.865 1204.4035_8.865 846.6352_8.854
## 2 6697.935 6983.009 162977.6 4851.083 3632.331
## 826.5716_8.868 1599.2099_8.871 788.6177_8.867 875.7207_8.87 919.7465_8.87
## 2 8242.287 6221.98 865810.6 7441.585 15927.8
## 1600.2146_8.869 905.7311_8.871 1598.2061_8.871 1576.2251_8.873
## 2 4016.72 29489.88 5958.527 23088.73
## 1577.2283_8.872 863.6838_8.874 846.6653_8.88 846.6641_8.878 920.75_8.87
## 2 23334.67 7788.028 6441.184 6441.184 8393.018
## 862.6249_8.847 798.635_8.894 1562.15_8.919 1541.1702_8.921 1540.1672_8.92
## 2 2585.278 6228.352 2620.241 3824.149 4577.397
## 1574.2638_8.923 1573.2611_8.925 1599.2772_8.928 1600.2791_8.929
## 2 6542.399 6040.388 5463.161 5027.115
## 752.5592_8.969 1540.1695_8.922 634.54_8.941 1424.1384_8.941 835.6566_8.968
## 2 121846.2 4577.397 11649.02 1106.273 16823.72
## 807.6349_8.948 1570.2972_8.954 1571.3005_8.952 785.6542_8.953 1618.2939_8.956
## 2 100125.8 4251.078 3762.899 522479.3 2913.59
## 1619.2973_8.957 1597.3162_8.956 1226.4736_8.96 1596.3127_8.957 902.7673_8.957
## 2 2636.041 6456.792 2384.73 7183.553 4785.488
## 864.6463_8.959 1622.3273_8.96 1623.3314_8.96 1538.2067_8.962 811.6703_8.961
## 2 3853.001 3328.855 2920.397 8198.008 452180.2
## 1537.2035_8.961 942.7986_8.961 928.7831_8.964 1563.2185_8.966 1564.2222_8.965
## 2 8158.631 3112.762 4864.024 6779.179 7188.461
## 1559.1849_8.966 820.6364_8.966 1560.1891_8.966 1505.1127_8.971 752.5594_8.973
## 2 4831.348 5543.612 4781.248 3282.363 121846.2
## 833.6507_8.971 1504.1092_8.971 1585.2008_8.971 1586.2043_8.972 839.6622_8.973
## 2 90670.38 3353.871 3973.039 4660.113 11185.8
## 1526.0908_8.976 1527.094_8.977 853.6786_8.975 853.6786_8.974 774.5404_8.982
## 2 2261.368 2447.814 6491.64 6065.305 33071
## 901.6373_8.994 796.5222_9.007 773.6516_9.025 848.6521_9.028 870.6336_9.039
## 2 5242.931 6539.751 5768.31 10455.57 3922.008
## 778.5756_9.056 800.5558_9.058 639.4955_9.064 634.5399_9.061 814.6319_9.087
## 2 21589.4 9973.236 9008.702 17308.44 45744.32
## 728.5588_9.081 772.5221_9.085 815.6606_9.084 814.6318_9.087 836.6136_9.086
## 2 36172.36 4829.609 6896.445 43821.04 12368.03
## 815.6352_9.088 976.684_9.115 889.5795_9.114 889.5796_9.114 906.6063_9.114
## 2 23855.86 3964.211 10294.85 10294.85 19006.69
## 911.5614_9.119 907.6093_9.114 761.6526_9.142 860.6131_9.152 838.6301_9.147
## 2 15033.86 9790.684 13240.48 3334.685 10393.55
## 761.6524_9.144 783.6349_9.156 824.6527_9.167 798.6374_9.202 787.6674_9.202
## 2 14064.4 4326.388 3069.503 10325.25 11397.39
## 837.6831_9.217 746.5685_9.239 836.6135_9.214 1134.7862_9.281 805.6784_9.291
## 2 2259.97 23541.21 5999.759 2264.969 3160.882
## 831.694_9.318 799.6686_9.32 821.6501_9.321 853.6758_9.321 825.6815_9.335
## 2 9682.384 64151.51 19314.07 4403.143 4570.61
## 795.6345_9.346 773.6531_9.348 796.6374_9.346 829.6788_9.393 790.5588_9.389
## 2 31471.45 106267.2 15768.11 2430.643 29043.04
## 812.5409_9.393 835.6652_9.434 813.6834_9.437 911.5609_9.505 748.6211_9.504
## 2 14324.15 2862.271 5873.96 3171.01 8226.154
## 906.6086_9.509 830.6471_9.551 825.6817_9.564 825.682_9.561 825.6778_9.575
## 2 4015.066 2937.108 5497.752 5497.752 5497.752
## 799.6692_9.585 821.6503_9.585 889.6375_9.586 824.6537_9.596 865.5795_9.626
## 2 126674.4 40410.24 5655.457 8026.033 9856.34
## 882.6062_9.627 605.5497_9.629 887.5614_9.632 610.5401_9.655 874.6674_9.727
## 2 11044.41 12343.13 13329.28 28221.02 3739.477
## 946.718_9.767 822.6412_9.776 994.7158_9.773 972.7339_9.773 610.5401_9.784
## 2 4369.626 3652.028 3394.066 6745.506 26610.01
## 575.5027_9.782 775.6661_9.814 848.6521_9.817 802.5715_9.823 780.578_9.823
## 2 7375.394 3828.128 6023.061 7545.51 20116.21
## 837.6724_9.906 636.5558_9.845 877.6374_9.86 1454.1868_9.88 1428.1716_9.868
## 2 21972.99 42947.85 7760.845 2822.004 3884.678
## 790.6308_9.873 836.6696_9.91 809.6507_9.876 825.6241_9.886 1229.9993_9.9
## 2 10953.72 68698.66 142217.1 5103.176 5663.727
## 1216.4896_9.892 1596.3093_9.895 822.6567_9.894 904.7835_9.894 787.6701_9.896
## 2 6419.074 2667.233 9440.671 7669.869 764947.4
## 918.7987_9.897 1575.3325_9.895 1597.3131_9.894 1216.9911_9.896
## 2 3990.714 9844.885 4571.203 6257.887
## 1574.3292_9.897 1610.32_9.892 1218.0011_9.9 1205.4975_9.898 1601.3486_9.899
## 2 9586.805 2037.454 2942.346 2605.5 15603.96
## 1624.8418_9.901 1612.335_9.9 1600.3452_9.899 1622.3263_9.899 1204.9948_9.897
## 2 1690.118 3212.694 17856.85 6902.972 2218.285
## 1623.3297_9.9 1626.36_9.901 1229.4977_9.901 823.1597_9.895 1636.3352_9.901
## 2 6103.94 7419.634 5963.986 9982.173 1853.384
## 1218.5051_9.902 813.6866_9.902 1611.8348_9.901 851.64_9.902 1648.3413_9.902
## 2 2684.094 690934.4 1809.095 6419.02 2774.843
## 944.8145_9.903 1635.8345_9.901 930.7989_9.903 1627.364_9.902 1231.5117_9.905
## 2 4155.017 2170.793 6697.064 7962.128 1438.79
## 1649.3448_9.904 1242.5053_9.906 835.6666_9.91 1637.3363_9.913 1624.3343_9.899
## 2 2797.678 2477.214 129321.4 1596.268 4526.23
## 903.6531_9.918 1454.1867_9.904 1455.1905_9.914 811.657_9.886 900.6836_9.96
## 2 7390.53 2822.004 2837.323 22864.78 3004.962
## 824.6526_9.963 816.6467_9.969 846.6342_9.978 850.668_9.982 1260.0333_9.997
## 2 22027.61 9653.991 6442.004 21643.29 1689.841
## 636.5558_9.995 877.6374_9.988 872.6492_9.995 641.5114_9.997 874.6655_10.042
## 2 56788.64 7760.845 6610.988 25429.38 4552.474
## 1285.8106_10.085 1237.8134_10.084 1259.7951_10.087 1263.8289_10.086
## 2 3408.22 6988.395 5009.238 4262.39
## 848.6549_10.11 839.6989_10.178 802.5716_10.204 893.6243_10.204
## 2 3186.075 6096.698 5188.358 5797.206
## 903.6531_10.207 780.5795_10.219 835.6664_10.22 1242.5051_10.22
## 2 8449.854 22486.07 103869.8 2082.82
## 813.6854_10.226 1649.3444_10.225 1626.3599_10.225 1627.3632_10.227
## 2 381365.5 2159.219 3409.696 2932.786
## 930.7988_10.228 993.7984_10.238 876.6835_10.245 850.6799_10.276
## 2 3466.205 1542.351 22272.34 10435.37
## 827.6998_10.277 849.6809_10.279 790.6874_10.339 837.6789_10.324
## 2 60154.34 14998.08 22034.11 9877.133
## 789.6837_10.343 817.7051_10.344 1263.8289_10.349 789.6834_10.343
## 2 40494.28 6994.667 5731.129 40494.28
## 1136.8021_10.373 784.6654_10.38 810.6812_10.387 827.6999_10.389
## 2 3619.193 49122.83 25851.02 26534.71
## 1392.1715_10.401 823.6664_10.409 918.7994_10.407 1624.3414_10.412
## 2 3190.6 62043.82 4696.219 2813.871
## 801.686_10.409 1602.3605_10.41 1603.364_10.411 612.5558_10.456 577.5187_10.46
## 2 692194.6 11764.94 12013.23 77427.25 32375.08
## 1251.8293_10.466 576.5712_10.467 1273.811_10.468 638.5716_10.5
## 2 8640.006 5095.905 3408.723 165529.4
## 827.6999_10.502 828.702_10.497 603.5343_10.505 1061.8543_10.506 974.75_10.504
## 2 25615.83 15173.97 34431.88 3343.002 11808.7
## 1281.8397_10.519 620.5974_10.522 602.5849_10.522 646.6126_10.526
## 2 6460.258 18829.71 5623.001 11654.99
## 798.681_10.534 876.6836_10.537 837.6818_10.567 1631.3955_10.568
## 2 32928.6 16258.83 39000.64 28180.23
## 1630.3921_10.568 815.7025_10.568 932.8152_10.571 1693.3899_10.581
## 2 28998.01 1358551 14281.75 3901.89
## 1694.3938_10.581 1265.8449_10.582 1266.8482_10.583 878.6996_10.587
## 2 3288.189 12453.42 8237.125 66347.16
## 852.6843_10.599 812.6971_10.626 634.6121_10.629 794.6868_10.627
## 2 28785.61 79744.41 13066.2 8050.009
## 829.7155_10.657 604.6026_10.678 622.6132_10.675 648.6287_10.678
## 2 93435.59 24810.28 92957.06 65970.8
## 630.6177_10.682 750.7353_10.695 766.6703_10.657 636.6288_10.754
## 2 16101.65 13713.27 2498.589 61143.04
## 618.6182_10.756 1298.1983_10.827 1297.1947_10.828 1301.284_10.831
## 2 18534.63 14894.16 15831.92 9458.521
## 650.6449_10.83 632.6339_10.83 1300.2808_10.831 1323.2657_10.832
## 2 231980 61316.71 10455.6 7166.766
## 1322.2624_10.832 737.7496_10.83 751.765_10.833 652.6596_10.839
## 2 7450.369 99716.04 43819.77 29194.06
## 698.6194_10.879 369.3508_10.879 822.6568_10.855 706.612_10.9 367.3357_10.902
## 2 7585.75 7600.824 1118.715 503.6229 2734.666
## 1312.1372_10.911 536.437_10.971 537.4409_10.971 794.7051_10.958
## 2 1260.354 15194.12 6173.038 1532.064
## 764.6932_10.985 750.6771_10.977 723.7681_10.986 990.7559_11.04
## 2 3286.64 4052.782 6541.254 503.6229
## 916.7378_11.078 986.8154_11.075 966.7541_11.082 790.6914_11.103
## 2 4055.856 2141.574 1308.728 14667.1
## 871.6776_11.111 866.723_11.11 897.6938_11.125 1012.8318_11.122
## 2 1971.72 11646.06 2771.518 2667.541
## 942.7538_11.123 816.7069_11.125 886.7839_11.127 992.7701_11.13
## 2 7002.996 24550.12 8966.745 1220.198
## 892.7383_11.126 947.7089_11.125 1026.8485_11.128 893.7418_11.129
## 2 13834.8 1734.781 1478.241 9333.282
## 847.6774_11.153 842.723_11.155 912.7986_11.157 1077.8922_11.168
## 2 4512.683 35983.47 12624.66 3036.969
## 1052.864_11.17 923.7092_11.174 988.8322_11.176 918.754_11.178 873.694_11.187
## 2 1462.379 4076.415 5921.241 24098.13 7523.448
## 873.6937_11.187 1038.8471_11.18 868.7389_11.19 938.8159_11.193 537.4408_11.2
## 2 7523.448 1416.362 59879.36 20733.04 10269.49
## 536.437_11.201 973.7251_11.18 899.7091_11.203 964.8324_11.205 978.8481_11.206
## 2 22663.33 1255.917 9732.242 18701.9 8140.438
## 894.7548_11.21 895.7581_11.209 949.7248_11.221 792.7074_11.221
## 2 68870.35 46552.75 3564.008 53668
## 1028.8632_11.223 1014.8477_11.222 944.7698_11.221 994.7855_11.222
## 2 2733.207 5224.73 21662.38 1416.049
## 965.8354_11.205 1064.8634_11.211 823.6785_11.238 818.7234_11.241
## 2 14816.66 1068.808 12250.72 122100.3
## 888.8006_11.24 979.8509_11.21 880.718_11.248 1651.3848_11.251 966.8463_11.305
## 2 42924.87 7065.987 25625.6 1242.25 78760.45
## 1676.3968_11.262 1677.4008_11.264 849.6939_11.262 958.844_11.264
## 2 1796.038 1726.64 20410.24 4713.466
## 972.8574_11.264 844.7397_11.265 914.817_11.265 928.8321_11.265 984.86_11.28
## 2 2451.91 204800.8 66042.93 29528.29 4351.066
## 970.786_11.284 875.7095_11.292 870.7558_11.296 940.8331_11.297
## 2 7937.148 30375.43 324633.8 108197.9
## 925.7249_11.296 954.8481_11.299 996.8_11.288 990.8484_11.304 920.7705_11.302
## 2 10130.74 44881.93 1424.071 21417.84 85782.57
## 967.8518_11.315 1004.8642_11.302 896.7714_11.316 980.8638_11.316
## 2 52803.07 8446.431 248298.5 32281.8
## 980.8635_11.316 966.8486_11.316 896.7712_11.316 901.7251_11.318
## 2 32281.8 78760.45 248298.5 24962.91
## 832.7384_11.319 1030.8788_11.336 1016.8631_11.335 946.7855_11.335
## 2 20185.07 5249.511 12976.75 45298.57
## 951.7402_11.339 863.7092_11.343 858.7545_11.345 972.8_11.345 1678.4082_11.291
## 2 5979.087 4331.861 38934.54 6095.989 1496.688
## 897.7743_11.318 794.7232_11.353 864.8008_11.354 1603.3857_11.359
## 2 159883.8 83499.66 35786.23 1324.06
## 1602.3814_11.358 884.7697_11.358 1629.4012_11.367 1628.3971_11.365
## 2 1567.764 22661.8 2671.462 3046.245
## 820.7395_11.368 942.849_11.418 927.7399_11.374 1654.4129_11.376
## 2 301490.2 478812.6 12192.02 4399.808
## 1655.4167_11.377 934.7878_11.376 992.8634_11.384 851.7097_11.384
## 2 4814.808 3561.133 46830.57 37883.41
## 968.863_11.436 1006.8788_11.385 1680.4284_11.386 922.7862_11.386
## 2 199250.5 19874.99 8051.829 174604.8
## 1681.4323_11.387 846.7559_11.39 930.848_11.39 916.8333_11.39 872.7731_11.418
## 2 7821.17 682281.1 114990.1 257165.2 1616522
## 877.7253_11.418 956.8644_11.419 942.8496_11.419 872.7324_11.418 924.8_11.503
## 2 60751.11 203498.6 478812.6 9127.002 217531.5
## 898.7872_11.44 982.8793_11.44 968.8643_11.442 971.877_11.497 903.7406_11.457
## 2 669157.7 92480.07 199250.5 17941.21 37241.99
## 834.7543_11.459 1018.8786_11.465 948.8005_11.465 1032.894_11.465
## 2 47546.99 10522.3 41912.89 4827.447
## 953.7558_11.474 974.8146_11.474 860.7704_11.477 865.7253_11.477
## 2 3773.431 6077.163 140002.2 11221.68
## 936.8015_11.483 891.7406_11.489 886.7858_11.492 929.7558_11.499
## 2 4311.123 8177.318 89998.27 14997.75
## 994.8787_11.507 1020.8931_11.508 1008.8943_11.508 1034.9093_11.511
## 2 61630.42 7597.849 25257.67 3466.29
## 1633.4321_11.51 924.8016_11.511 950.8156_11.512 1628.4765_11.515
## 2 2025.455 228336 27785.38 1713.184
## 1658.4443_11.517 892.8326_11.516 1659.4478_11.517 822.755_11.516
## 2 6331.303 145534.3 5374.612 287093.4
## 1653.489_11.522 1654.4923_11.523 1684.4596_11.522 1685.4637_11.522
## 2 3217.005 3502.013 7813.414 11191.29
## 951.819_11.515 848.7717_11.527 1679.5046_11.53 1680.5083_11.531
## 2 20169.02 967240.2 6822.774 7704.743
## 876.6825_11.558 877.7965_11.559 944.8656_11.558 958.8804_11.56
## 2 3346.467 122302.7 890314 398300.2
## 874.7892_11.559 874.7461_11.559 875.5569_11.56 876.854_11.56 820.6961_11.574
## 2 3172820 15439.89 2351.354 21837.2 4369.521
## 879.7408_11.578 912.8727_11.58 688.6024_11.581 900.803_11.585 970.8801_11.587
## 2 48040.11 6471.074 21189.06 1112878 325268.1
## 984.8952_11.587 905.7564_11.605 950.8167_11.564 1020.8946_11.578
## 2 149427.6 44519.53 27785.38 7168.371
## 976.8319_11.618 927.8194_11.647 862.7859_11.623 1034.911_11.594
## 2 3982.127 77872.82 222026.7 2552.789
## 955.7715_11.624 996.8931_11.636 1010.9091_11.638 888.8014_11.642
## 2 1778.225 34972.3 17250.75 190564.5
## 1416.1569_11.643 714.6184_11.643 1417.1607_11.643 893.7563_11.645
## 2 1047.74 102431.2 1195.386 23155.99
## 931.7715_11.646 926.8159_11.647 719.5732_11.65 1022.9086_11.654
## 2 15659.2 114237.9 6053.102 8046.453
## 1036.925_11.654 1366.1408_11.655 957.7865_11.655 1367.1442_11.655
## 2 4327.82 2090.963 4914.232 1783.41
## 952.831_11.66 978.8456_11.66 1316.1245_11.664 1317.1277_11.664
## 2 27333.94 2786.061 2743.785 3071.889
## 664.6011_11.668 1311.1692_11.669 1312.1724_11.668 928.8306_11.753
## 2 34742.44 3528.94 2835.189 67667.66
## 1689.4913_11.681 1688.4878_11.681 1657.5142_11.686 1683.5318_11.692
## 2 5808.51 4370.88 503.6229 5529.812
## 1684.5352_11.693 960.8944_11.707 946.8797_11.708 876.8035_11.712
## 2 6383.304 428250.2 899821.8 3226126
## 879.8107_11.711 902.8168_11.73 876.7617_11.713 878.587_11.712 972.8948_11.737
## 2 120675.1 895668.6 17487.13 2670.459 269066.1
## 902.8182_11.738 986.9104_11.739 1554.3397_11.745 1555.3432_11.744
## 2 1224359 127751.5 4530.259 3650.815
## 1041.9353_11.754 1363.2025_11.753 1364.2059_11.753 690.6188_11.754
## 2 2514.979 24606.14 25608.94 252108.4
## 673.5905_11.756 1369.1611_11.766 1368.1577_11.767 907.7722_11.767
## 2 8385.006 16062.58 14618.86 60527.93
## 928.8317_11.769 695.5729_11.772 1012.925_11.773 998.9084_11.775
## 2 67667.66 14446.94 9726.109 22197.71
## 933.7875_11.787 1339.2017_11.786 1344.1575_11.789 1345.161_11.79
## 2 11010.68 3396.515 8898.468 7879.225
## 895.7721_11.798 890.8172_11.801 1268.1254_11.807 1295.1453_11.81
## 2 39354.18 222009.8 4658.194 10834.82
## 1294.1419_11.81 1263.1707_11.815 991.9207_11.815 1289.1866_11.82
## 2 11917.9 3417.174 4366.934 12816.24
## 1290.1901_11.819 1320.1579_11.832 1321.1613_11.836 369.3517_11.844
## 2 14974.65 39232.81 39623.7 182142.1
## 1322.1646_11.831 666.619_11.852 370.3549_11.851 1298.1748_11.858
## 2 24688.49 524568.4 54212.87 6319.687
## 1299.1785_11.856 1017.9359_11.856 1318.2126_11.857 1316.2069_11.859
## 2 6578.469 25785.38 28259.07 165839.2
## 1315.2035_11.858 1341.2177_11.895 1342.2211_11.896 692.6335_11.899
## 2 159782 7678.696 8293.413 46673.77
## 693.6368_11.899 1532.3557_11.908 1346.1728_11.908 1347.1765_11.909
## 2 23759.46 3606.448 6072.426 6827.92
## 904.8336_11.91 1558.3711_11.912 988.9264_11.914 974.911_11.915
## 2 709680.4 4606.458 99856.62 219563.4
## 930.8479_11.919 1000.9255_11.924 1014.9413_11.924 1334.1724_11.947
## 2 77632.02 26061.62 12150.83 1344.198
## 956.8628_11.946 1308.157_11.948 1322.172_12.024 1350.2033_12.028
## 2 8415.506 1872.269 4146.23 2119.103
## 1324.1888_12.056 1325.1924_12.055 1299.1762_12.078 370.3549_12.075
## 2 27354.78 28560.82 15242.02 53898.55
## 1019.9516_12.075 1319.2346_12.075 1303.2104_12.075 1302.2067_12.075
## 2 54721.55 125490.6 15517.75 16427.43
## 1320.238_12.076 668.6342_12.074 1277.1948_12.078 369.3518_12.077
## 2 123558.4 166722.6 12821.56 186192.3
## 1294.2217_12.077 1268.2053_12.079 1293.2183_12.078 1326.1956_12.056
## 2 50350.77 4661.689 53581.95 15722.65
## 1267.2019_12.079 1276.1913_12.079 1250.1754_12.08 642.6107_12.077
## 2 5505.106 13411.3 4338.924 13245.47
## 1298.1727_12.089 1272.1559_12.098 1028.9572_12.137 963.8345_12.135
## 2 15940.22 2504.315 7405.451 6037.557
## 984.8939_12.149 1329.2228_12.335 1328.2189_12.336
## 2 4043.26 2327.458 2329.194
connection <- find_connections(data = data_notame,
features = features,
corr_thresh = 0.95,
rt_window = 1/60,
name_col = "mz_rt",
mz_col = "mz",
rt_col = "rt")## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000
## [1] 1100
## [1] 1200
## [1] 1300
## [1] 1400
## [1] 1500
## [1] 1600
## [1] 1700
## [1] 1800
## [1] 1900
## [1] 2000
## [1] 2100
## [1] 2200
## [1] 2300
## [1] 2400
## x y cor rt_diff mz_diff
## 1 167.0891_0.631 256.1695_0.637 0.9818637 0.006 89.0804
## 2 167.0891_0.631 272.1651_0.645 0.9613305 0.014 105.0760
## 3 256.1695_0.637 272.1651_0.645 0.9517330 0.008 15.9956
## 4 265.1183_0.643 287.1003_0.648 0.9564272 0.005 21.9820
## 5 634.4366_0.676 648.4519_0.687 0.9572685 0.011 14.0153
## 6 648.4519_0.687 736.5043_0.684 0.9511110 -0.003 88.0524
## 242 components found
##
## Component 100 / 242
## Component 200 / 242
## 55 components found
##
## 5 components found
# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")
# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures
# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)## [1] 388
# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
dplyr::select(-"row_id") %>%
pivot_wider(names_from = mz_rt,
values_from = peak_height)
# list of reduced features
clusternames <- cluster_features$mz_rt
# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]
# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)
tibble(imp_clust)## # A tibble: 85 × 2,038
## sample `189.1598_0.513` `104.1071_0.537` `184.0946_0.54` `162.1126_0.545`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 x5109_b1_… 5394. 173277. 51759. 144958.
## 2 x5101_b1_… 3299. 129830. 68092. 159756.
## 3 x5109_b3_… 6079. 173316. 58650. 140823.
## 4 x5103_b3_… 4923. 161982. 53680. 140005.
## 5 x5108_b1_… 3050. 127211. 55879. 141761.
## 6 x5113_b1_… 4468. 125901. 51706. 123194.
## 7 x5112_b3_… 3506. 162774. 51842. 135057.
## 8 x5105_b1_… 7545. 168842. 55074. 144985.
## 9 x5110_b3_… 4195. 193137. 45673. 136221.
## 10 x5110_b1_… 4086. 163577. 51901. 125073
## # ℹ 75 more rows
## # ℹ 2,033 more variables: `204.1231_0.589` <dbl>, `265.2403_0.591` <dbl>,
## # `144.102_0.595` <dbl>, `118.0864_0.597` <dbl>, `337.0619_0.611` <dbl>,
## # `132.102_0.612` <dbl>, `623.1355_0.614` <dbl>, `116.0707_0.623` <dbl>,
## # `645.1222_0.627` <dbl>, `293.0984_0.628` <dbl>, `100.0757_0.629` <dbl>,
## # `160.0606_0.629` <dbl>, `353.0342_0.629` <dbl>, `172.1333_0.62` <dbl>,
## # `120.0808_0.631` <dbl>, `166.0861_0.629` <dbl>, `258.1105_0.632` <dbl>, …
Let’s see how our clustered data looks now compared to the original
# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
ggplot(aes(x = rt,
y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z, neutral",
title = "mz across RT for all features after clustering"))# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt,
plot_mzvsrt_postcluster,
nrow = 2))# meta data columns
str_meta <- colnames(imp_metabind_clust[,4:13])
# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
mutate_at(str_meta, as.character)
# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"
# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
pivot_longer(cols = 15:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund")
# structure check
head(imp_metabind_clust_tidy)## # A tibble: 6 × 16
## subject treatment tomato_or_control sex bmi age tot_chol ldl_chol
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 5107 beta tomato M 23.81 68 177 85.4
## 2 5107 beta tomato M 23.81 68 177 85.4
## 3 5107 beta tomato M 23.81 68 177 85.4
## 4 5107 beta tomato M 23.81 68 177 85.4
## 5 5107 beta tomato M 23.81 68 177 85.4
## 6 5107 beta tomato M 23.81 68 177 85.4
## # ℹ 8 more variables: hdl_chol <chr>, triglycerides <chr>, glucose <chr>,
## # SBP <chr>, DBP <chr>, sample <chr>, mz_rt <chr>, rel_abund <dbl>
imp_metabind_clust_tidy %>%
ggplot(aes(x = subject, y = rel_abund, color = treatment)) +
geom_boxplot(alpha = 0.6) +
scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (+) Feature Abundances by Sample",
subtitle = "Unscaled data",
y = "Relative abundance")Will need to log transform in order to normalize and actually see the data
imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
mutate(rel_abund_log2 = log2(rel_abund))(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
ggplot(aes(x = sample, y = rel_abund_log2, color = treatment)) +
geom_boxplot(alpha = 0.6) +
scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (+) Feature Abundances by Sample",
subtitle = "Log2 transformed data",
y = "Relative abundance"))# filtered and imputed data after notame clustering, transposed
features_forQCcorr <- imp_clust %>%
dplyr::select(!subject) %>%
t() %>%
as.data.frame() %>%
row_to_names(row_number = "find_header")
# log2 transform
log2_features_forQCcorr <- features_forQCcorr %>%
mutate_all(as.numeric) %>%
log2()
# write csv to manually edit
write.csv(log2_features_forQCcorr,
"Notame/feaures_fromR_forDC_1.csv",
row.names = TRUE)Import corrected df (edited so that “mz_rt” could be the row name for row 1)
log2_features_forQCcorr_new <- read.csv("Notame/feaures_forR_forDC_editedmzrt_2.csv",
header = FALSE,
row.names = 1)
log2_features_forQCcorr_new <- log2_features_forQCcorr_new %>%
rownames_to_column(var = "mz_rt") %>%
row_to_names(row_number = 1) %>%
separate(col = mz_rt,
into = c("mz", "rt"),
sep = "_")
write.csv(log2_features_forQCcorr_new,
"Notame/features_fromR_forDC_3.csv",
row.names = TRUE)# separate sampleID and injection order
pheno_data <- imp_clust[1] %>%
separate(col = sample,
into = c("sample", "injection_order"),
# last underscore
sep = "_(?!.*_)")
# make inj order column numeric
pheno_data <- pheno_data %>%
mutate_at("injection_order", as.numeric)
t_pheno_data <- as.data.frame(t(pheno_data))
write.csv(t_pheno_data,
"Notame/pheno_df.csv",
row.names = TRUE)Combine pheno and feature dfs manually in excel to create metaboset df.
xf## Import Metaboset
#make sure when converting csv to xlsx that you save as a new file, don't just change the name of the file
metaboset <- read_from_excel("Notame/metaboset.xlsx",
split_by = c("column", "Ion mode"))## INFO [2025-10-06 20:59:15] Detecting corner position
## INFO [2025-10-06 20:59:15] Corner detected correctly at row 3, column D
## INFO [2025-10-06 20:59:15]
## Extracting sample information from rows 1 to 3 and columns E to CK
## INFO [2025-10-06 20:59:15] Replacing spaces in sample information column names with underscores (_)
## INFO [2025-10-06 20:59:15] Naming the last column of sample information "Datafile"
## INFO [2025-10-06 20:59:15]
## Extracting feature information from rows 4 to 2040 and columns A to D
## INFO [2025-10-06 20:59:15] Creating Split column from column, Ion mode
## INFO [2025-10-06 20:59:15] Feature_ID column not found, creating feature IDs
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Creating feature IDs from Split, m/z and retention time
## INFO [2025-10-06 20:59:15] Replacing dots (.) in feature information column names with underscores (_)
## INFO [2025-10-06 20:59:15]
## Extracting feature abundances from rows 4 to 2040 and columns E to CK
## INFO [2025-10-06 20:59:15]
## Checking sample information
## INFO [2025-10-06 20:59:15] QC column generated from rows containing 'QC'
## INFO [2025-10-06 20:59:15] Sample ID autogenerated from injection orders and prefix ID_
## INFO [2025-10-06 20:59:15] Checking that feature abundances only contain numeric values
## INFO [2025-10-06 20:59:15]
## Checking feature information
## INFO [2025-10-06 20:59:15] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-10-06 20:59:15] Checking that m/z and retention time values are reasonable
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
#construct Metaboset
modes <- construct_metabosets(exprs = metaboset$exprs,
pheno_data = metaboset$pheno_data,
feature_data = metaboset$feature_data, group_col = "Class")## Initializing the object(s) with unflagged features
## INFO [2025-10-06 20:59:15]
## Checking feature information
## INFO [2025-10-06 20:59:15] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-10-06 20:59:15] Checking that feature abundances only contain numeric values
## INFO [2025-10-06 20:59:15] Setting row and column names of exprs based on feature and pheno data
# ordered by injection
(qualityBPs_b4correction <- plot_sample_boxplots(mode, order_by = c("Class", "Injection_order"), title = "Uncorrected feature abundance"))#ordered by class
plot_sample_boxplots(mode, order_by = "Injection_order", title = "Uncorrected feature abundance")drift correction takes up to 2 minutes
## INFO [2025-10-06 20:59:21]
## 0% of features flagged for low detection rate
## INFO [2025-10-06 20:59:21]
## Starting drift correction at 2025-10-06 20:59:21.360597
## INFO [2025-10-06 20:59:28] Drift correction performed at 2025-10-06 20:59:28.162445
## INFO [2025-10-06 20:59:30] Inspecting drift correction results 2025-10-06 20:59:30.37915
## INFO [2025-10-06 20:59:35] Drift correction results inspected at 2025-10-06 20:59:35.392669
## INFO [2025-10-06 20:59:35]
## Drift correction results inspected, report:
## Drift_corrected: 100%
output is percent of the features that were drift corrected. The remaining “low-quality” percent represents features for which the DC did not improve the RSD and D-ratio of the original data.
## INFO [2025-10-06 20:59:37] Inspecting drift correction results 2025-10-06 20:59:37.548909
## INFO [2025-10-06 20:59:44] Drift correction results inspected at 2025-10-06 20:59:44.638607
## INFO [2025-10-06 20:59:44]
## Drift correction results inspected, report:
## Drift_corrected: 79%, Low_quality: 21%
(qualityBPs_compared <- ggarrange(qualityBPs_b4correction, qualityBPS_driftcorrection,
ncol = 1, nrow = 2))Manually edit the df so it only has mass, rt, and sample columns
metabdata_corrected_MZ_RT <- metabdata_corrected %>%
mutate(mass = round(metabdata_corrected$mass, digits = 4), # Decrease number of decimals for m/z & rt
rt = round(metabdata_corrected$rt, digits = 3),
.before=1,
.keep="unused") %>%
unite(mz_rt, c(mass, rt), remove=TRUE) # Combine m/z & rt with _ in betweenI want the new “metabdata_corrected_t” df to look just like “imp_metabind_clust_log2” df
# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)# bind metadata columns to the new drift corrected df
DC_imp_metabind_clust_log2 <- full_join(imp_metabind_clust_log2[,c(1:14)],
metabdata_corrected_t,
by = "sample")
# fix QC subject names
DC_imp_metabind_clust_log2$subject <- str_replace_all(DC_imp_metabind_clust_log2$subject, "c", "qc")
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
mutate_at("subject", str_sub, start=1, end=4)
# make feature abundances numeric
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
mutate_at(15:ncol(.), as.numeric)PCA.DC_imp_metabind_clust_log2 <- PCA(DC_imp_metabind_clust_log2, # wide data
quali.sup = 1:14, # remove qualitative variables
graph = FALSE, # don't graph
scale.unit = FALSE) # don't scale, already transformed data
# PCA summary
kable(summary(PCA.DC_imp_metabind_clust_log2))##
## Call:
## PCA(X = DC_imp_metabind_clust_log2, scale.unit = FALSE, quali.sup = 1:14,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 55.606 43.326 28.623 24.069 19.730 18.346 16.482
## % of var. 15.234 11.869 7.841 6.594 5.405 5.026 4.515
## Cumulative % of var. 15.234 27.103 34.944 41.538 46.943 51.969 56.484
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 10.320 9.932 9.033 8.458 7.767 6.841 5.992
## % of var. 2.827 2.721 2.475 2.317 2.128 1.874 1.641
## Cumulative % of var. 59.311 62.032 64.507 66.824 68.952 70.826 72.467
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 5.723 5.235 4.940 4.621 4.249 3.946 3.809
## % of var. 1.568 1.434 1.353 1.266 1.164 1.081 1.044
## Cumulative % of var. 74.035 75.469 76.822 78.088 79.253 80.334 81.377
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 3.513 3.241 3.130 2.863 2.716 2.425 2.418
## % of var. 0.962 0.888 0.858 0.784 0.744 0.664 0.662
## Cumulative % of var. 82.340 83.228 84.085 84.869 85.613 86.278 86.940
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 2.320 2.175 2.066 1.967 1.794 1.744 1.598
## % of var. 0.636 0.596 0.566 0.539 0.491 0.478 0.438
## Cumulative % of var. 87.576 88.172 88.737 89.276 89.768 90.245 90.683
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 1.556 1.448 1.383 1.331 1.303 1.226 1.193
## % of var. 0.426 0.397 0.379 0.365 0.357 0.336 0.327
## Cumulative % of var. 91.109 91.506 91.885 92.250 92.607 92.943 93.269
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 1.173 1.115 1.069 1.033 1.011 0.957 0.940
## % of var. 0.321 0.306 0.293 0.283 0.277 0.262 0.257
## Cumulative % of var. 93.591 93.896 94.189 94.472 94.749 95.011 95.269
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54 Dim.55 Dim.56
## Variance 0.895 0.851 0.819 0.808 0.789 0.754 0.706
## % of var. 0.245 0.233 0.224 0.221 0.216 0.207 0.193
## Cumulative % of var. 95.514 95.747 95.971 96.193 96.409 96.616 96.809
## Dim.57 Dim.58 Dim.59 Dim.60 Dim.61 Dim.62 Dim.63
## Variance 0.694 0.678 0.654 0.629 0.611 0.574 0.550
## % of var. 0.190 0.186 0.179 0.172 0.167 0.157 0.151
## Cumulative % of var. 96.999 97.185 97.364 97.536 97.704 97.861 98.012
## Dim.64 Dim.65 Dim.66 Dim.67 Dim.68 Dim.69 Dim.70
## Variance 0.540 0.513 0.493 0.490 0.470 0.463 0.452
## % of var. 0.148 0.141 0.135 0.134 0.129 0.127 0.124
## Cumulative % of var. 98.160 98.300 98.435 98.570 98.698 98.825 98.949
## Dim.71 Dim.72 Dim.73 Dim.74 Dim.75 Dim.76 Dim.77
## Variance 0.414 0.381 0.350 0.334 0.331 0.305 0.304
## % of var. 0.114 0.104 0.096 0.092 0.091 0.084 0.083
## Cumulative % of var. 99.063 99.167 99.263 99.355 99.445 99.529 99.612
## Dim.78 Dim.79 Dim.80 Dim.81 Dim.82 Dim.83 Dim.84
## Variance 0.288 0.271 0.268 0.262 0.211 0.116 0.000
## % of var. 0.079 0.074 0.073 0.072 0.058 0.032 0.000
## Cumulative % of var. 99.691 99.765 99.839 99.910 99.968 100.000 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 20.874 | 0.213 0.001 0.000 | 10.940 3.250 0.275
## 2 | 27.204 | -11.312 2.707 0.173 | 13.374 4.857 0.242
## 3 | 29.175 | -20.166 8.604 0.478 | -13.918 5.260 0.228
## 4 | 24.206 | -13.883 4.078 0.329 | -9.813 2.615 0.164
## 5 | 15.779 | -1.225 0.032 0.006 | 0.003 0.000 0.000
## 6 | 19.091 | -5.076 0.545 0.071 | -6.393 1.110 0.112
## 7 | 31.853 | -23.048 11.239 0.524 | -10.881 3.215 0.117
## 8 | 18.257 | -9.826 2.043 0.290 | -2.831 0.218 0.024
## 9 | 18.984 | 0.289 0.002 0.000 | 4.862 0.642 0.066
## 10 | 22.033 | -4.972 0.523 0.051 | 10.184 2.816 0.214
## Dim.3 ctr cos2
## 1 | -0.041 0.000 0.000 |
## 2 | 0.267 0.003 0.000 |
## 3 | -2.625 0.283 0.008 |
## 4 | 5.101 1.069 0.044 |
## 5 | 1.988 0.162 0.016 |
## 6 | 2.827 0.328 0.022 |
## 7 | -6.916 1.966 0.047 |
## 8 | -0.337 0.005 0.000 |
## 9 | -1.261 0.065 0.004 |
## 10 | 1.317 0.071 0.004 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 189.1598_0.513 | 0.106 0.020 0.035 | -0.049 0.006 0.007 | 0.060 0.013
## 104.1071_0.537 | 0.048 0.004 0.061 | -0.010 0.000 0.003 | 0.014 0.001
## 184.0946_0.54 | -0.005 0.000 0.001 | 0.024 0.001 0.019 | 0.022 0.002
## 162.1126_0.545 | 0.028 0.001 0.030 | 0.023 0.001 0.021 | 0.013 0.001
## 204.1231_0.589 | 0.004 0.000 0.000 | 0.130 0.039 0.207 | 0.060 0.013
## 265.2403_0.591 | -0.018 0.001 0.014 | -0.005 0.000 0.001 | 0.003 0.000
## 144.102_0.595 | 0.307 0.169 0.044 | 0.076 0.013 0.003 | -0.100 0.035
## 118.0864_0.597 | 0.025 0.001 0.009 | 0.075 0.013 0.081 | 0.016 0.001
## 337.0619_0.611 | 0.000 0.000 0.000 | 0.019 0.001 0.058 | 0.015 0.001
## 132.102_0.612 | 0.022 0.001 0.008 | -0.098 0.022 0.171 | -0.013 0.001
## cos2
## 189.1598_0.513 0.011 |
## 104.1071_0.537 0.005 |
## 184.0946_0.54 0.017 |
## 162.1126_0.545 0.007 |
## 204.1231_0.589 0.044 |
## 265.2403_0.591 0.000 |
## 144.102_0.595 0.005 |
## 118.0864_0.597 0.004 |
## 337.0619_0.611 0.038 |
## 132.102_0.612 0.003 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 5101 | 17.414 | -1.630 0.009 -0.311 | 6.585 0.143 1.423
## 5102 | 14.960 | 0.172 0.000 0.033 | -5.929 0.157 -1.282
## 5103 | 19.338 | -11.845 0.375 -2.260 | 4.470 0.053 0.966
## 5104 | 18.160 | -8.310 0.209 -1.586 | -8.516 0.220 -1.841
## 5105 | 16.415 | -9.390 0.327 -1.792 | -2.961 0.033 -0.640
## 5107 | 21.298 | -5.550 0.068 -1.059 | 12.157 0.326 2.628
## 5108 | 21.982 | -14.027 0.407 -2.676 | 3.989 0.033 0.862
## 5109 | 24.521 | -17.024 0.482 -3.248 | -11.866 0.234 -2.565
## 5110 | 14.906 | -8.665 0.338 -1.653 | 3.600 0.058 0.778
## 5111 | 16.322 | 6.183 0.144 1.180 | 0.501 0.001 0.108
## Dim.3 cos2 v.test
## 5101 | 0.475 0.001 0.126 |
## 5102 | 1.902 0.016 0.506 |
## 5103 | -6.347 0.108 -1.688 |
## 5104 | 1.245 0.005 0.331 |
## 5105 | 5.011 0.093 1.333 |
## 5107 | 0.113 0.000 0.030 |
## 5108 | -2.232 0.010 -0.594 |
## 5109 | 1.238 0.003 0.329 |
## 5110 | 0.604 0.002 0.161 |
## 5111 | -7.713 0.223 -2.051 |
| Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5101 | | | 17.414 | | | -1.630 | 0.009 | -0.311 | | | 6.585 | 0.143 | 1.423 | | | 0.475 | 0.001 | 0.126 | | |
| 5102 | | | 14.960 | | | 0.172 | 0.000 | 0.033 | | | -5.929 | 0.157 | -1.282 | | | 1.902 | 0.016 | 0.506 | | |
| 5103 | | | 19.338 | | | -11.845 | 0.375 | -2.260 | | | 4.470 | 0.053 | 0.966 | | | -6.347 | 0.108 | -1.688 | | |
| 5104 | | | 18.160 | | | -8.310 | 0.209 | -1.586 | | | -8.516 | 0.220 | -1.841 | | | 1.245 | 0.005 | 0.331 | | |
| 5105 | | | 16.415 | | | -9.390 | 0.327 | -1.792 | | | -2.961 | 0.033 | -0.640 | | | 5.011 | 0.093 | 1.333 | | |
| 5107 | | | 21.298 | | | -5.550 | 0.068 | -1.059 | | | 12.157 | 0.326 | 2.628 | | | 0.113 | 0.000 | 0.030 | | |
| 5108 | | | 21.982 | | | -14.027 | 0.407 | -2.676 | | | 3.989 | 0.033 | 0.862 | | | -2.232 | 0.010 | -0.594 | | |
| 5109 | | | 24.521 | | | -17.024 | 0.482 | -3.248 | | | -11.866 | 0.234 | -2.565 | | | 1.238 | 0.003 | 0.329 | | |
| 5110 | | | 14.906 | | | -8.665 | 0.338 | -1.653 | | | 3.600 | 0.058 | 0.778 | | | 0.604 | 0.002 | 0.161 | | |
| 5111 | | | 16.322 | | | 6.183 | 0.144 | 1.180 | | | 0.501 | 0.001 | 0.108 | | | -7.713 | 0.223 | -2.051 | | |
# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2$ind$coord)
# bind back metadata from cols 1-14
PC_coord_QC_log2 <- bind_cols(DC_imp_metabind_clust_log2[,1:14], PC_coord_QC_log2)
# grab some variance explained
importance_QC <- PCA.DC_imp_metabind_clust_log2$eig
# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)Using FactoExtra package
| eigenvalue | variance.percent | cumulative.variance.percent | |
|---|---|---|---|
| Dim.1 | 55.6062932 | 15.2335459 | 15.23355 |
| Dim.2 | 43.3257818 | 11.8692552 | 27.10280 |
| Dim.3 | 28.6228608 | 7.8413366 | 34.94414 |
| Dim.4 | 24.0687248 | 6.5937145 | 41.53785 |
| Dim.5 | 19.7296407 | 5.4050067 | 46.94286 |
| Dim.6 | 18.3461676 | 5.0259992 | 51.96886 |
| Dim.7 | 16.4819103 | 4.5152791 | 56.48414 |
| Dim.8 | 10.3197899 | 2.8271439 | 59.31128 |
| Dim.9 | 9.9321320 | 2.7209436 | 62.03222 |
| Dim.10 | 9.0331512 | 2.4746646 | 64.50689 |
| Dim.11 | 8.4578886 | 2.3170693 | 66.82396 |
| Dim.12 | 7.7665970 | 2.1276874 | 68.95165 |
| Dim.13 | 6.8412777 | 1.8741928 | 70.82584 |
| Dim.14 | 5.9917494 | 1.6414615 | 72.46730 |
| Dim.15 | 5.7226639 | 1.5677445 | 74.03504 |
| Dim.16 | 5.2347639 | 1.4340826 | 75.46913 |
| Dim.17 | 4.9396562 | 1.3532367 | 76.82236 |
| Dim.18 | 4.6214994 | 1.2660765 | 78.08844 |
| Dim.19 | 4.2494361 | 1.1641484 | 79.25259 |
| Dim.20 | 3.9460214 | 1.0810269 | 80.33362 |
| Dim.21 | 3.8094183 | 1.0436040 | 81.37722 |
| Dim.22 | 3.5129854 | 0.9623951 | 82.33962 |
| Dim.23 | 3.2411405 | 0.8879222 | 83.22754 |
| Dim.24 | 3.1304509 | 0.8575984 | 84.08514 |
| Dim.25 | 2.8626531 | 0.7842342 | 84.86937 |
| Dim.26 | 2.7157455 | 0.7439884 | 85.61336 |
| Dim.27 | 2.4245963 | 0.6642270 | 86.27759 |
| Dim.28 | 2.4181283 | 0.6624550 | 86.94004 |
| Dim.29 | 2.3199122 | 0.6355484 | 87.57559 |
| Dim.30 | 2.1752966 | 0.5959304 | 88.17152 |
| Dim.31 | 2.0655342 | 0.5658606 | 88.73738 |
| Dim.32 | 1.9665669 | 0.5387481 | 89.27613 |
| Dim.33 | 1.7939804 | 0.4914674 | 89.76760 |
| Dim.34 | 1.7435959 | 0.4776644 | 90.24526 |
| Dim.35 | 1.5982665 | 0.4378509 | 90.68311 |
| Dim.36 | 1.5562239 | 0.4263332 | 91.10944 |
| Dim.37 | 1.4484581 | 0.3968104 | 91.50625 |
| Dim.38 | 1.3832387 | 0.3789433 | 91.88520 |
| Dim.39 | 1.3310969 | 0.3646588 | 92.24986 |
| Dim.40 | 1.3032682 | 0.3570351 | 92.60689 |
| Dim.41 | 1.2257640 | 0.3358025 | 92.94269 |
| Dim.42 | 1.1926630 | 0.3267344 | 93.26943 |
| Dim.43 | 1.1730456 | 0.3213601 | 93.59079 |
| Dim.44 | 1.1153255 | 0.3055475 | 93.89634 |
| Dim.45 | 1.0692251 | 0.2929181 | 94.18925 |
| Dim.46 | 1.0325166 | 0.2828617 | 94.47212 |
| Dim.47 | 1.0110964 | 0.2769935 | 94.74911 |
| Dim.48 | 0.9566692 | 0.2620830 | 95.01119 |
| Dim.49 | 0.9396273 | 0.2574143 | 95.26861 |
| Dim.50 | 0.8951120 | 0.2452192 | 95.51383 |
| Dim.51 | 0.8512604 | 0.2332059 | 95.74703 |
| Dim.52 | 0.8190131 | 0.2243716 | 95.97140 |
| Dim.53 | 0.8083077 | 0.2214388 | 96.19284 |
| Dim.54 | 0.7894961 | 0.2162853 | 96.40913 |
| Dim.55 | 0.7543496 | 0.2066568 | 96.61578 |
| Dim.56 | 0.7058323 | 0.1933653 | 96.80915 |
| Dim.57 | 0.6939990 | 0.1901235 | 96.99927 |
| Dim.58 | 0.6778229 | 0.1856921 | 97.18497 |
| Dim.59 | 0.6537120 | 0.1790868 | 97.36405 |
| Dim.60 | 0.6288967 | 0.1722885 | 97.53634 |
| Dim.61 | 0.6113481 | 0.1674810 | 97.70382 |
| Dim.62 | 0.5738358 | 0.1572044 | 97.86103 |
| Dim.63 | 0.5503131 | 0.1507603 | 98.01179 |
| Dim.64 | 0.5396885 | 0.1478496 | 98.15964 |
| Dim.65 | 0.5134275 | 0.1406553 | 98.30029 |
| Dim.66 | 0.4929250 | 0.1350386 | 98.43533 |
| Dim.67 | 0.4902101 | 0.1342948 | 98.56962 |
| Dim.68 | 0.4701340 | 0.1287949 | 98.69842 |
| Dim.69 | 0.4627609 | 0.1267750 | 98.82519 |
| Dim.70 | 0.4524660 | 0.1239547 | 98.94915 |
| Dim.71 | 0.4143090 | 0.1135014 | 99.06265 |
| Dim.72 | 0.3813885 | 0.1044828 | 99.16713 |
| Dim.73 | 0.3499514 | 0.0958705 | 99.26300 |
| Dim.74 | 0.3340413 | 0.0915118 | 99.35452 |
| Dim.75 | 0.3309475 | 0.0906643 | 99.44518 |
| Dim.76 | 0.3054864 | 0.0836891 | 99.52887 |
| Dim.77 | 0.3043578 | 0.0833799 | 99.61225 |
| Dim.78 | 0.2876988 | 0.0788161 | 99.69106 |
| Dim.79 | 0.2711268 | 0.0742762 | 99.76534 |
| Dim.80 | 0.2680656 | 0.0734375 | 99.83878 |
| Dim.81 | 0.2616145 | 0.0716702 | 99.91045 |
| Dim.82 | 0.2109577 | 0.0577926 | 99.96824 |
| Dim.83 | 0.1158401 | 0.0317348 | 99.99998 |
| Dim.84 | 0.0000861 | 0.0000236 | 100.00000 |
# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = factor(treatment, levels = c("control", "beta", "red", "QC")),
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("lightgreen", "orange", "tomato", "lightgray")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_withQC/PC1_withQC) +
labs(x = glue::glue("PC1: {PC1_withQC}%"),
y = glue::glue("PC2: {PC2_withQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data"))DC_imp_metabind_clust_log2_noQCs <- DC_imp_metabind_clust_log2 %>%
filter(treatment != "QC")
PCA.DC_imp_metabind_clust_log2_noQCs <- PCA(DC_imp_metabind_clust_log2_noQCs, # wide data
quali.sup=1:14, # remove qualitative variables
graph=FALSE, # don't graph
scale.unit=FALSE) # don't scale, we already did this
# look at summary
kable(summary(PCA.DC_imp_metabind_clust_log2_noQCs))##
## Call:
## PCA(X = DC_imp_metabind_clust_log2_noQCs, scale.unit = FALSE,
## quali.sup = 1:14, graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 63.124 52.598 32.601 23.938 22.416 20.081 12.491
## % of var. 15.661 13.049 8.088 5.939 5.561 4.982 3.099
## Cumulative % of var. 15.661 28.710 36.798 42.737 48.299 53.281 56.380
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 12.079 11.055 10.144 9.321 8.387 7.467 6.945
## % of var. 2.997 2.743 2.517 2.313 2.081 1.853 1.723
## Cumulative % of var. 59.377 62.119 64.636 66.948 69.029 70.882 72.605
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 6.330 6.079 5.613 5.174 4.853 4.618 4.296
## % of var. 1.571 1.508 1.392 1.284 1.204 1.146 1.066
## Cumulative % of var. 74.175 75.683 77.076 78.360 79.564 80.709 81.775
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 4.010 3.782 3.479 3.295 2.939 2.900 2.825
## % of var. 0.995 0.938 0.863 0.817 0.729 0.720 0.701
## Cumulative % of var. 82.770 83.708 84.572 85.389 86.118 86.838 87.538
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 2.638 2.544 2.364 2.171 2.112 1.960 1.918
## % of var. 0.654 0.631 0.586 0.539 0.524 0.486 0.476
## Cumulative % of var. 88.193 88.824 89.411 89.949 90.473 90.959 91.435
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 1.782 1.676 1.636 1.567 1.481 1.435 1.431
## % of var. 0.442 0.416 0.406 0.389 0.367 0.356 0.355
## Cumulative % of var. 91.878 92.293 92.699 93.088 93.455 93.811 94.166
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 1.351 1.296 1.266 1.241 1.162 1.122 1.080
## % of var. 0.335 0.322 0.314 0.308 0.288 0.278 0.268
## Cumulative % of var. 94.501 94.823 95.137 95.445 95.733 96.012 96.280
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54 Dim.55 Dim.56
## Variance 1.026 0.985 0.985 0.943 0.931 0.856 0.844
## % of var. 0.254 0.244 0.244 0.234 0.231 0.212 0.209
## Cumulative % of var. 96.534 96.779 97.023 97.257 97.488 97.700 97.910
## Dim.57 Dim.58 Dim.59 Dim.60 Dim.61 Dim.62 Dim.63
## Variance 0.817 0.780 0.755 0.739 0.684 0.660 0.641
## % of var. 0.203 0.193 0.187 0.183 0.170 0.164 0.159
## Cumulative % of var. 98.112 98.306 98.493 98.677 98.846 99.010 99.169
## Dim.64 Dim.65 Dim.66 Dim.67 Dim.68 Dim.69
## Variance 0.600 0.594 0.562 0.556 0.537 0.500
## % of var. 0.149 0.147 0.139 0.138 0.133 0.124
## Cumulative % of var. 99.318 99.465 99.605 99.743 99.876 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 20.832 | 1.319 0.039 0.004 | 10.907 3.231 0.274
## 2 | 26.942 | -10.666 2.575 0.157 | 13.458 4.919 0.250
## 3 | 28.516 | -19.057 8.219 0.447 | -13.776 5.154 0.233
## 4 | 23.768 | -13.331 4.022 0.315 | -9.702 2.556 0.167
## 5 | 15.482 | 0.043 0.000 0.000 | -0.031 0.000 0.000
## 6 | 18.649 | -3.865 0.338 0.043 | -6.393 1.110 0.118
## 7 | 31.228 | -22.079 11.032 0.500 | -10.718 3.120 0.118
## 8 | 17.890 | -9.136 1.889 0.261 | -2.765 0.208 0.024
## 9 | 18.776 | 1.822 0.075 0.009 | 4.810 0.628 0.066
## 10 | 21.952 | -4.262 0.411 0.038 | 10.211 2.832 0.216
## Dim.3 ctr cos2
## 1 | 1.131 0.056 0.003 |
## 2 | -1.834 0.147 0.005 |
## 3 | -3.779 0.626 0.018 |
## 4 | 5.796 1.472 0.059 |
## 5 | 4.997 1.094 0.104 |
## 6 | 5.488 1.320 0.087 |
## 7 | -11.334 5.629 0.132 |
## 8 | -1.407 0.087 0.006 |
## 9 | 1.919 0.161 0.010 |
## 10 | 1.374 0.083 0.004 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 189.1598_0.513 | 0.078 0.010 0.017 | -0.053 0.005 0.008 | 0.013 0.001
## 104.1071_0.537 | 0.055 0.005 0.068 | -0.012 0.000 0.003 | 0.037 0.004
## 184.0946_0.54 | -0.012 0.000 0.004 | 0.026 0.001 0.021 | 0.016 0.001
## 162.1126_0.545 | 0.033 0.002 0.034 | 0.025 0.001 0.020 | 0.028 0.002
## 204.1231_0.589 | 0.002 0.000 0.000 | 0.144 0.039 0.208 | 0.086 0.022
## 265.2403_0.591 | -0.023 0.001 0.019 | -0.006 0.000 0.001 | -0.008 0.000
## 144.102_0.595 | 0.243 0.093 0.024 | 0.084 0.014 0.003 | -0.260 0.207
## 118.0864_0.597 | 0.025 0.001 0.007 | 0.083 0.013 0.082 | 0.017 0.001
## 337.0619_0.611 | -0.007 0.000 0.007 | 0.021 0.001 0.067 | 0.004 0.000
## 132.102_0.612 | 0.022 0.001 0.007 | -0.109 0.022 0.172 | -0.022 0.001
## cos2
## 189.1598_0.513 0.000 |
## 104.1071_0.537 0.030 |
## 184.0946_0.54 0.007 |
## 162.1126_0.545 0.025 |
## 204.1231_0.589 0.074 |
## 265.2403_0.591 0.003 |
## 144.102_0.595 0.028 |
## 118.0864_0.597 0.003 |
## 337.0619_0.611 0.002 |
## 132.102_0.612 0.007 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 5101 | 17.122 | -0.213 0.000 -0.038 | 6.548 0.146 1.286
## 5102 | 14.884 | 1.148 0.006 0.206 | -5.963 0.161 -1.171
## 5103 | 18.649 | -10.429 0.313 -1.870 | 4.525 0.059 0.889
## 5104 | 17.990 | -8.003 0.198 -1.435 | -8.444 0.220 -1.659
## 5105 | 16.049 | -8.890 0.307 -1.594 | -2.885 0.032 -0.567
## 5107 | 21.110 | -4.674 0.049 -0.838 | 12.183 0.333 2.393
## 5108 | 21.374 | -12.876 0.363 -2.309 | 4.078 0.036 0.801
## 5109 | 23.911 | -16.194 0.459 -2.904 | -11.739 0.241 -2.306
## 5110 | 14.250 | -7.541 0.280 -1.352 | 3.638 0.065 0.715
## 5111 | 16.413 | 7.756 0.223 1.391 | 0.379 0.001 0.074
## Dim.3 cos2 v.test
## 5101 | 3.618 0.045 0.903 |
## 5102 | 4.207 0.080 1.050 |
## 5103 | -8.042 0.186 -2.007 |
## 5104 | -0.595 0.001 -0.149 |
## 5105 | 5.463 0.116 1.363 |
## 5107 | -0.351 0.000 -0.088 |
## 5108 | -2.850 0.018 -0.711 |
## 5109 | 1.008 0.002 0.252 |
## 5110 | 1.196 0.007 0.298 |
## 5111 | -6.722 0.168 -1.677 |
| Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5101 | | | 17.122 | | | -0.213 | 0.000 | -0.038 | | | 6.548 | 0.146 | 1.286 | | | 3.618 | 0.045 | 0.903 | | |
| 5102 | | | 14.884 | | | 1.148 | 0.006 | 0.206 | | | -5.963 | 0.161 | -1.171 | | | 4.207 | 0.080 | 1.050 | | |
| 5103 | | | 18.649 | | | -10.429 | 0.313 | -1.870 | | | 4.525 | 0.059 | 0.889 | | | -8.042 | 0.186 | -2.007 | | |
| 5104 | | | 17.990 | | | -8.003 | 0.198 | -1.435 | | | -8.444 | 0.220 | -1.659 | | | -0.595 | 0.001 | -0.149 | | |
| 5105 | | | 16.049 | | | -8.890 | 0.307 | -1.594 | | | -2.885 | 0.032 | -0.567 | | | 5.463 | 0.116 | 1.363 | | |
| 5107 | | | 21.110 | | | -4.674 | 0.049 | -0.838 | | | 12.183 | 0.333 | 2.393 | | | -0.351 | 0.000 | -0.088 | | |
| 5108 | | | 21.374 | | | -12.876 | 0.363 | -2.309 | | | 4.078 | 0.036 | 0.801 | | | -2.850 | 0.018 | -0.711 | | |
| 5109 | | | 23.911 | | | -16.194 | 0.459 | -2.904 | | | -11.739 | 0.241 | -2.306 | | | 1.008 | 0.002 | 0.252 | | |
| 5110 | | | 14.250 | | | -7.541 | 0.280 | -1.352 | | | 3.638 | 0.065 | 0.715 | | | 1.196 | 0.007 | 0.298 | | |
| 5111 | | | 16.413 | | | 7.756 | 0.223 | 1.391 | | | 0.379 | 0.001 | 0.074 | | | -6.722 | 0.168 | -1.677 | | |
# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2_noQCs$ind$coord)
# bind back metadata from cols 1-14
PC_coord_noQCs_log2 <- bind_cols(DC_imp_metabind_clust_log2_noQCs[,1:14], PC_coord_noQCs_log2)
# grab some variance explained
importance_noQC <- PCA.DC_imp_metabind_clust_log2_noQCs$eig
# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)Using FactoExtra
# wrangling data prior to plot for ease
PC_coord_noQCs_log2 <- PC_coord_noQCs_log2 %>%
mutate(sample2 = sample) %>%
mutate_at("sample2", str_sub, start=7, end=8) %>%
mutate(period = sample2) %>%
unite(treatment_period, "treatment", "period", sep = "_", remove = FALSE) %>%
dplyr::select(!sample2) %>%
mutate_at("period", as.factor) %>%
# relevel factors
mutate(treatment_period = fct_relevel(treatment_period, c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3")),
treatment = fct_relevel(treatment, c("control", "beta", "red")))(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs"))(PCA_faceted_noQCs <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "treatment_period",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ period) +
theme(strip.background = element_rect(fill="white")))PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = sex,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("purple", "blue")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ period) +
theme(strip.background = element_rect(fill="white"))(PCA_faceted_noQCs2 <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "Intervention timepoint",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data faceted by treatment group ") +
facet_wrap( ~ treatment))Export plot
ggsave(plot = PCA_faceted_noQCs2,
filename = "plots and figures/PCA-faceted-by-group-lipidomicsPOS.svg",
bg = "transparent",
height = 3,
width = 8)PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = sex,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("purple", "blue")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ treatment) +
theme(strip.background = element_rect(fill="white"))(PC_coord_facetsex <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = treatment_period,
text = sample)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan3", "orangered2",
"lavenderblush3", "darkred")) +
scale_color_manual(values = "black") +
theme_bw() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "sex",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, No QCs") +
facet_wrap( ~ sex) +
theme(strip.background = element_rect(fill="white")))This type of PCA accounts for the structure of paired data, allowing for a more accurate assessment of biological differences between treatment groups, not differences attributed to the natural variation between individuals.
See http://mixomics.org/methods/multilevel/ for more info.
Data_forMPCA <- DC_imp_metabind_clust_log2_noQCs %>%
mutate_at("subject", as.factor) %>%
mutate(sample2 = sample,
b_c = carotenoids$b_c_nmol_l_plasma,
lyc = carotenoids$lyc_nmol_l_plasma,
apo13one = carotenoids$apo13one_nmol_l_plasma,
retinol = carotenoids$retinol_nmol_l_plasma,
total_carot = carotenoids$total_carotenoids) %>%
mutate_at("sample2", str_sub, start=7, end=8) %>%
mutate(period = sample2) %>%
dplyr::select(c(1:14), period, b_c, lyc, apo13one, retinol, total_carot, everything()) %>%
dplyr::select(!sample2)
summary(as.factor(Data_forMPCA$subject))## 5101 5102 5103 5104 5105 5107 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 5134 5135 5136
## 2 2 2
## [1] "subject" "treatment" "tomato_or_control"
## [4] "sex" "bmi" "age"
## [7] "tot_chol" "ldl_chol" "hdl_chol"
## [10] "triglycerides" "glucose" "SBP"
## [13] "DBP" "sample" "period"
## [16] "b_c" "lyc" "apo13one"
## [19] "retinol" "total_carot"
mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
scale = FALSE,
center = FALSE)
plotIndiv(mixOmicsPCA.result,
ind.names = Data_forMPCA$subject,
group = Data_forMPCA$treatment,
legend = TRUE,
legend.title = "Treatment",
title = 'Regular PCA, Lipidomics C18 (+)')With all data
multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:20))],
multilevel = Data_forMPCA$subject,
scale = FALSE,
center = FALSE,ncomp = 10)
plotIndiv(multilevelPCA.result,
ind.names = Data_forMPCA$period,
group = Data_forMPCA$treatment,
legend = TRUE,
legend.title = "Treatment",
title = 'Multilevel PCA, Lipidomics C18 (+)',comp = c(1,2))# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_forPCAtools <- Data_forMPCA %>%
# select only sample ID and feature columns
dplyr::select(sample,
21:ncol(.)) %>%
# transpose
t() %>%
# convert back to df
as.data.frame()
names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make samp;e IDs column names
imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row
# create metadata df suitable for PCAtools pckg
metadata_forPCAtools <- Data_forMPCA[,1:20]
metadata_forPCAtools <- metadata_forPCAtools %>%
unite("treatment_period", c("treatment", "period"), sep = "_", remove = FALSE) %>%
column_to_rownames("sample")
# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(colnames(imp_clust_omicsdata_forPCAtools), rownames(metadata_forPCAtools))
# reorder col names in abundance df so that it matches metadata
log2_abundances_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[,order_forPCAtools] %>%
# change abundance df to numeric
mutate_all(as.numeric)# pca
p <- PCAtools::pca(log2_abundances_reordered_forPCAtools,
metadata = metadata_forPCAtools,
scale = FALSE, # using scaled data already (log2 transformed)
)
biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment',
colkey = c("control" = "lightgreen",
"beta" = "orange",
"red" = "tomato"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE,ntopLoadings = 10)# pca
biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'period',
colkey = c("b1" = "gray",
"b3" = "pink"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE)biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan3",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses.",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE, ntopLoadings = 10)biplot(p,
lab = paste0(metadata_forPCAtools$subject),
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan3",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data. 95% CI ellipses.",
showLoadings = TRUE, ntopLoadings = 10)Let’s explore a little more
How many PCs do we need to capture at least 80% variance?
## PC20
## 20
This shows we’d need quite a few PCs to capture most of the variance.
Here, we will look at separations for several components at once using pairs plots.
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'treatment_period',
colkey = c("control_b1" = "darkseagreen2",
"control_b3" = "darkgreen",
"beta_b1" = "tan",
"beta_b3" = "orangered2",
"red_b1" = "lavenderblush3",
"red_b3" = "darkred"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'period',
colkey = c("b1" = "darkgray",
"b3" = "pink"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))Are there any obvious clusterings when colored by sex?
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'sex',
colkey = c("M" = "red",
"F" = "purple"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset.
Again: How many PCs do we need to capture at least 80% variance?
## PC20
## 20
eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
metavars = colnames(metadata_forPCAtools),
col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
cexCorval = 0.7,
colCorval = 'white',
fontCorval = 2,
posLab = 'bottomleft',
rotLabX = 45,
posColKey = 'top',
cexLabColKey = 1.5,
scale = TRUE,
main = 'PC1-15 metadata correlations',
colFrame = 'white',
plotRsquared = FALSE) eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
metavars = colnames(metadata_forPCAtools),
col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
cexCorval = 1.2,
fontCorval = 2,
posLab = 'all',
rotLabX = 45,
scale = TRUE,
main = bquote(Principal ~ component ~ Spearman ~ r^2 ~ metadata ~ correlates),
plotRsquared = TRUE,
corFUN = 'spearman',
corUSE = 'pairwise.complete.obs',
corMultipleTestCorrection = 'BH',
signifSymbols = c('****', '***', '**', '*', ''),
signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))df_for_stats$treatment_period <- as.factor(df_for_stats$treatment_period)
trt_anova_output_df <- df_for_stats %>%
dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
anova_test(rel_abund_log2 ~ treatment_period, wid = subject,
detailed = TRUE) %>%
adjust_pvalue(method = "BH") %>%
as.data.frame()
trt_anova_sig <- trt_anova_output_df %>%
filter(p.adj < .05)
head(trt_anova_sig)## mz_rt Effect SSn SSd DFn DFd F p p<.05
## 1 118.0864_0.597 treatment_period 1.548 4.336 5 64 4.571 0.0010000 *
## 2 1341.0272_6.83 treatment_period 6.978 16.964 5 64 5.265 0.0004120 *
## 3 1363.015_6.835 treatment_period 6.298 15.876 5 64 5.078 0.0005550 *
## 4 1365.0287_6.836 treatment_period 6.375 17.802 5 64 4.584 0.0010000 *
## 5 1531.6527_7.711 treatment_period 2.010 5.072 5 64 5.073 0.0005590 *
## 6 1585.2008_8.971 treatment_period 2.329 4.069 5 64 7.326 0.0000176 *
## ges p.adj
## 1 0.263 0.03772222
## 2 0.291 0.03496850
## 3 0.284 0.03772222
## 4 0.264 0.03772222
## 5 0.284 0.03772222
## 6 0.364 0.00298760
## [1] 54
# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
filter(treatment == "control") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
filter(p < 0.05)
kable(ctrl_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1017.6873_3.095 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.790664 | 10 | 0.0191000 | * |
| 1020.8931_11.508 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.297219 | 10 | 0.0080500 | ** |
| 1026.8485_11.128 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.807671 | 10 | 0.0185000 | * |
| 1028.9572_12.137 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.256003 | 10 | 0.0477000 | * |
| 1034.9093_11.511 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.930130 | 10 | 0.0150000 | * |
| 1034.911_11.594 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.354362 | 10 | 0.0073100 | ** |
| 1039.6688_3.106 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.105943 | 10 | 0.0111000 | * |
| 104.1071_0.537 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.874240 | 10 | 0.0030900 | ** |
| 1041.9353_11.754 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.387721 | 10 | 0.0381000 | * |
| 1065.848_6.509 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.476544 | 10 | 0.0327000 | * |
| 1083.7892_6.33 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.626526 | 10 | 0.0253000 | * |
| 1151.3639_7.711 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.766235 | 10 | 0.0036800 | ** |
| 1151.8656_7.71 | rel_abund_log2 | b1 | b3 | 11 | 11 | 7.499461 | 10 | 0.0000206 | **** |
| 118.0864_0.597 | rel_abund_log2 | b1 | b3 | 11 | 11 | -8.101096 | 10 | 0.0000105 | **** |
| 1192.8412_6.709 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.454356 | 10 | 0.0340000 | * |
| 1193.3431_6.71 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.969116 | 10 | 0.0141000 | * |
| 1193.9116_8.867 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.399487 | 10 | 0.0373000 | * |
| 1204.4035_8.865 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.000891 | 10 | 0.0133000 | * |
| 1204.9948_9.897 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.434276 | 10 | 0.0352000 | * |
| 1205.4054_8.863 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.643491 | 10 | 0.0045100 | ** |
| 1205.4975_9.898 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.151023 | 10 | 0.0103000 | * |
| 1211.3587_7.312 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.477648 | 10 | 0.0327000 | * |
| 1216.4896_9.892 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.927260 | 10 | 0.0151000 | * |
| 1226.4736_8.96 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.308458 | 10 | 0.0436000 | * |
| 1227.3893_7.956 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.532025 | 10 | 0.0298000 | * |
| 1227.7565_5.834 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.885803 | 10 | 0.0162000 | * |
| 1231.5117_9.905 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.335208 | 10 | 0.0417000 | * |
| 1237.8134_10.084 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.496035 | 10 | 0.0317000 | * |
| 1242.5051_10.22 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.050200 | 10 | 0.0122000 | * |
| 1242.5053_9.906 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.634050 | 10 | 0.0250000 | * |
| 1251.8293_10.466 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.704581 | 10 | 0.0221000 | * |
| 1259.7951_10.087 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.820917 | 10 | 0.0000462 | **** |
| 1263.8289_10.349 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.117356 | 10 | 0.0109000 | * |
| 1267.2019_12.079 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.236068 | 10 | 0.0493000 | * |
| 1272.1559_12.098 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.901447 | 10 | 0.0029500 | ** |
| 1297.1947_10.828 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.293987 | 10 | 0.0447000 | * |
| 1298.1727_12.089 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.151109 | 10 | 0.0103000 | * |
| 1298.1983_10.827 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.440455 | 10 | 0.0012500 | ** |
| 1299.1762_12.078 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.977143 | 10 | 0.0026100 | ** |
| 1302.2067_12.075 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.820547 | 10 | 0.0181000 | * |
| 1322.2624_10.832 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.326599 | 10 | 0.0015000 | ** |
| 1324.1888_12.056 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.726371 | 10 | 0.0213000 | * |
| 1329.2228_12.335 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.942960 | 10 | 0.0147000 | * |
| 1339.0138_6.829 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.468721 | 10 | 0.0332000 | * |
| 1341.0272_6.83 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.998976 | 10 | 0.0025200 | ** |
| 1347.1765_11.909 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.203456 | 10 | 0.0094400 | ** |
| 1363.015_6.835 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.108668 | 10 | 0.0021200 | ** |
| 1365.0287_6.836 | rel_abund_log2 | b1 | b3 | 11 | 11 | 7.045136 | 10 | 0.0000352 | **** |
| 1367.1442_11.655 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.290500 | 10 | 0.0450000 | * |
| 1369.1611_11.766 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.373578 | 10 | 0.0390000 | * |
| 1389.0311_6.813 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.670679 | 10 | 0.0043100 | ** |
| 1444.1182_6.522 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.773396 | 10 | 0.0197000 | * |
| 1458.1721_6.714 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.865090 | 10 | 0.0168000 | * |
| 1470.0283_7.811 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.397023 | 10 | 0.0375000 | * |
| 1473.1717_7.517 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.362144 | 10 | 0.0398000 | * |
| 1475.1518_7.004 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.267897 | 10 | 0.0016400 | ** |
| 1476.1552_7.005 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.931828 | 10 | 0.0005940 | *** |
| 1481.1402_7.792 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.857176 | 10 | 0.0170000 | * |
| 1482.1063_6.531 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.656134 | 10 | 0.0044200 | ** |
| 1486.0818_6.746 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.377445 | 10 | 0.0388000 | * |
| 1487.1515_6.871 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.352899 | 10 | 0.0404000 | * |
| 1497.1674_7.486 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.974166 | 10 | 0.0139000 | * |
| 1504.14_8.066 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.677861 | 10 | 0.0232000 | * |
| 1509.1328_6.475 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.294904 | 10 | 0.0446000 | * |
| 1511.145_6.854 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.638300 | 10 | 0.0248000 | * |
| 1517.1985_7.759 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.351366 | 10 | 0.0405000 | * |
| 1520.1651_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.047982 | 10 | 0.0005010 | *** |
| 1522.0815_7.299 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.273527 | 10 | 0.0463000 | * |
| 1523.1825_7.536 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.354399 | 10 | 0.0403000 | * |
| 1524.1325_6.944 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.091632 | 10 | 0.0114000 | * |
| 1528.1659_7.665 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.710389 | 10 | 0.0219000 | * |
| 1530.1768_7.745 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.770219 | 10 | 0.0198000 | * |
| 1531.1683_7.726 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.528654 | 10 | 0.0299000 | * |
| 1531.181_7.771 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.118148 | 10 | 0.0109000 | * |
| 1531.2124_8.09 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.631046 | 10 | 0.0002180 | *** |
| 1531.6527_7.711 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.681205 | 10 | 0.0002040 | *** |
| 1532.1582_7.713 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.160777 | 10 | 0.0019500 | ** |
| 1535.1521_6.713 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.478310 | 10 | 0.0059400 | ** |
| 1542.1448_7.703 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.657994 | 10 | 0.0008970 | *** |
| 1542.645_7.698 | rel_abund_log2 | b1 | b3 | 11 | 11 | 7.110448 | 10 | 0.0000325 | **** |
| 1543.1481_7.702 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.306853 | 10 | 0.0079200 | ** |
| 1543.2104_7.94 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.750715 | 10 | 0.0205000 | * |
| 1544.1549_7.686 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.314713 | 10 | 0.0432000 | * |
| 1546.1749_7.78 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.755782 | 10 | 0.0037500 | ** |
| 1546.1772_8.02 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.737425 | 10 | 0.0038600 | ** |
| 1547.1793_7.789 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.361596 | 10 | 0.0398000 | * |
| 1547.1803_7.856 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.317510 | 10 | 0.0430000 | * |
| 1547.245_8.769 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.606359 | 10 | 0.0262000 | * |
| 1548.1302_6.87 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.439971 | 10 | 0.0349000 | * |
| 1548.6249_6.858 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.408658 | 10 | 0.0013200 | ** |
| 1550.1462_7.021 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.740372 | 10 | 0.0208000 | * |
| 1550.14_6.908 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.143230 | 10 | 0.0105000 | * |
| 1553.1308_7.035 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.042733 | 10 | 0.0124000 | * |
| 1554.1833_7.701 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.720627 | 10 | 0.0215000 | * |
| 1557.1328_6.71 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.039261 | 10 | 0.0125000 | * |
| 1558.1201_7.783 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.985060 | 10 | 0.0137000 | * |
| 1558.1357_6.713 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.291355 | 10 | 0.0081300 | ** |
| 1560.0999_6.725 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.098703 | 10 | 0.0113000 | * |
| 1562.1488_7.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.708787 | 10 | 0.0220000 | * |
| 1562.1493_7.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.050176 | 10 | 0.0122000 | * |
| 1563.1166_6.865 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.610726 | 10 | 0.0260000 | * |
| 1563.1535_7.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.184553 | 10 | 0.0097400 | ** |
| 1563.2185_8.966 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.733878 | 10 | 0.0211000 | * |
| 1565.1377_6.892 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.421521 | 10 | 0.0360000 | * |
| 1570.1778_7.936 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.381044 | 10 | 0.0385000 | * |
| 1572.1304_7.394 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.767814 | 10 | 0.0199000 | * |
| 1574.2638_8.923 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.471699 | 10 | 0.0060000 | ** |
| 1577.1237_6.867 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.437085 | 10 | 0.0350000 | * |
| 1577.6354_7.106 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.015619 | 10 | 0.0130000 | * |
| 1580.1032_7.792 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.874781 | 10 | 0.0165000 | * |
| 1585.2008_8.971 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.920967 | 10 | 0.0153000 | * |
| 1586.2043_8.972 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.476796 | 10 | 0.0059500 | ** |
| 1591.2114_7.764 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.903396 | 10 | 0.0157000 | * |
| 1592.2118_7.764 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.383741 | 10 | 0.0069600 | ** |
| 1595.6827_8.019 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.238139 | 10 | 0.0492000 | * |
| 1596.3093_9.895 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.519878 | 10 | 0.0304000 | * |
| 1596.6247_6.715 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.776857 | 10 | 0.0196000 | * |
| 1597.1201_6.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.018448 | 10 | 0.0024400 | ** |
| 1597.3131_9.894 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.621334 | 10 | 0.0255000 | * |
| 1598.1451_7.197 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.554611 | 10 | 0.0002430 | *** |
| 1604.1937_7.739 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.230493 | 10 | 0.0090100 | ** |
| 1606.677_8.009 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.450950 | 10 | 0.0342000 | * |
| 1616.16_7.754 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.691308 | 10 | 0.0226000 | * |
| 162.1126_0.545 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.932879 | 10 | 0.0150000 | * |
| 1621.1991_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.630060 | 10 | 0.0046100 | ** |
| 1621.2757_7.96 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.323910 | 10 | 0.0425000 | * |
| 1622.2784_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.131680 | 10 | 0.0107000 | * |
| 1623.3297_9.9 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.244637 | 10 | 0.0088000 | ** |
| 1624.3343_9.899 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.393566 | 10 | 0.0377000 | * |
| 1624.3414_10.412 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.394402 | 10 | 0.0068400 | ** |
| 1629.2009_7.725 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.251763 | 10 | 0.0086900 | ** |
| 1631.1866_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.271844 | 10 | 0.0464000 | * |
| 1647.2105_8.001 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.333932 | 10 | 0.0418000 | * |
| 1648.3413_9.902 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.045530 | 10 | 0.0123000 | * |
| 1649.3444_10.225 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.337395 | 10 | 0.0075200 | ** |
| 1685.4637_11.522 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.606833 | 10 | 0.0262000 | * |
| 1689.4913_11.681 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.555455 | 10 | 0.0286000 | * |
| 1693.3899_10.581 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.289356 | 10 | 0.0451000 | * |
| 205.1221_0.621 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.036155 | 10 | 0.0005090 | *** |
| 286.1437_1.13 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.184792 | 10 | 0.0097400 | ** |
| 293.0984_0.628 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.089539 | 10 | 0.0115000 | * |
| 308.1855_0.633 | rel_abund_log2 | b1 | b3 | 11 | 11 | -13.931210 | 10 | 0.0000001 | **** |
| 310.2012_0.633 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.698826 | 10 | 0.0224000 | * |
| 312.1597_1.413 | rel_abund_log2 | b1 | b3 | 11 | 11 | -7.879904 | 10 | 0.0000134 | **** |
| 331.0535_0.634 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.365045 | 10 | 0.0071800 | ** |
| 373.2483_1.147 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.117361 | 10 | 0.0109000 | * |
| 383.1533_0.931 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.296307 | 10 | 0.0445000 | * |
| 398.3413_0.639 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.925002 | 10 | 0.0152000 | * |
| 424.3418_1.822 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.594059 | 10 | 0.0268000 | * |
| 426.3213_1.384 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.974343 | 10 | 0.0139000 | * |
| 428.3732_2.759 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.119610 | 10 | 0.0109000 | * |
| 464.1914_0.656 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.096723 | 10 | 0.0113000 | * |
| 480.3446_3.381 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.772086 | 10 | 0.0197000 | * |
| 482.3601_3.403 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.232306 | 10 | 0.0496000 | * |
| 502.2902_3.355 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.782971 | 10 | 0.0194000 | * |
| 518.4564_5.944 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.686718 | 10 | 0.0008590 | *** |
| 521.4196_4.236 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.290164 | 10 | 0.0450000 | * |
| 522.3564_3.24 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.561755 | 10 | 0.0283000 | * |
| 526.2644_1.714 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.125405 | 10 | 0.0001120 | *** |
| 536.437_10.971 | rel_abund_log2 | b1 | b3 | 11 | 11 | 13.947401 | 10 | 0.0000001 | **** |
| 536.437_11.201 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.880596 | 10 | 0.0030600 | ** |
| 538.3859_4.051 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.764525 | 10 | 0.0036900 | ** |
| 542.3237_2.234 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.344564 | 10 | 0.0410000 | * |
| 544.3374_3.238 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.485266 | 10 | 0.0058700 | ** |
| 552.4018_4.301 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.556762 | 10 | 0.0285000 | * |
| 568.4266_4.64 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.679443 | 10 | 0.0042500 | ** |
| 577.5185_8.358 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.566190 | 10 | 0.0010300 | ** |
| 593.4757_4.54 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.666969 | 10 | 0.0000559 | **** |
| 593.4762_4.644 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.776202 | 10 | 0.0036200 | ** |
| 593.4768_4.312 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.861204 | 10 | 0.0031500 | ** |
| 595.4929_4.645 | rel_abund_log2 | b1 | b3 | 11 | 11 | 14.235717 | 10 | 0.0000001 | **** |
| 596.4154_4.906 | rel_abund_log2 | b1 | b3 | 11 | 11 | -7.086453 | 10 | 0.0000335 | **** |
| 597.5086_5.242 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.854831 | 10 | 0.0006670 | *** |
| 602.5849_10.522 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.292973 | 10 | 0.0448000 | * |
| 603.2923_2.783 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.259576 | 10 | 0.0474000 | * |
| 610.431_5.16 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.740268 | 10 | 0.0208000 | * |
| 617.4748_4.643 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.438551 | 10 | 0.0012600 | ** |
| 619.4903_4.953 | rel_abund_log2 | b1 | b3 | 11 | 11 | 9.332923 | 10 | 0.0000030 | **** |
| 622.6132_10.675 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.892456 | 10 | 0.0030000 | ** |
| 623.1355_0.614 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.704836 | 10 | 0.0221000 | * |
| 634.5399_9.061 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.604696 | 10 | 0.0263000 | * |
| 634.54_8.941 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.417715 | 10 | 0.0362000 | * |
| 645.1222_0.627 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.328483 | 10 | 0.0076400 | ** |
| 650.6449_10.83 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.864561 | 10 | 0.0031400 | ** |
| 652.6596_10.839 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.511019 | 10 | 0.0056200 | ** |
| 661.5282_5.288 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.403092 | 10 | 0.0371000 | * |
| 664.5997_4.075 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.440161 | 10 | 0.0348000 | * |
| 682.5516_8.574 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.275984 | 10 | 0.0461000 | * |
| 691.5742_6.403 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.254538 | 10 | 0.0086500 | ** |
| 692.5581_7.191 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.898210 | 10 | 0.0159000 | * |
| 692.6335_11.899 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.994384 | 10 | 0.0135000 | * |
| 693.6368_11.899 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.589768 | 10 | 0.0270000 | * |
| 705.5909_6.851 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.621539 | 10 | 0.0255000 | * |
| 714.6184_11.643 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.511412 | 10 | 0.0308000 | * |
| 715.5745_6.252 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.448506 | 10 | 0.0343000 | * |
| 717.5903_6.828 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.294410 | 10 | 0.0080900 | ** |
| 717.5903_7.018 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.808502 | 10 | 0.0007140 | *** |
| 718.5742_8.138 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.660488 | 10 | 0.0239000 | * |
| 718.5934_7.015 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.251334 | 10 | 0.0016900 | ** |
| 719.5732_11.65 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.352399 | 10 | 0.0405000 | * |
| 719.6006_7.392 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.923367 | 10 | 0.0001460 | *** |
| 724.5272_7.284 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.623312 | 10 | 0.0255000 | * |
| 727.5745_6.067 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.444692 | 10 | 0.0012500 | ** |
| 729.5912_6.714 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.055669 | 10 | 0.0121000 | * |
| 731.6047_7.112 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.493482 | 10 | 0.0318000 | * |
| 731.6073_7.511 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.013350 | 10 | 0.0130000 | * |
| 732.5542_6.723 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.253349 | 10 | 0.0479000 | * |
| 732.589_8.53 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.807446 | 10 | 0.0186000 | * |
| 733.6215_7.929 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.723050 | 10 | 0.0214000 | * |
| 738.5064_6.603 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.670328 | 10 | 0.0235000 | * |
| 740.5222_7.221 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.822173 | 10 | 0.0181000 | * |
| 742.5718_8.28 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.024335 | 10 | 0.0024200 | ** |
| 743.6054_7.227 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.783727 | 10 | 0.0007420 | *** |
| 745.6202_7.852 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.479042 | 10 | 0.0059300 | ** |
| 745.6214_8.115 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.101679 | 10 | 0.0001150 | *** |
| 746.5685_9.239 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.742176 | 10 | 0.0208000 | * |
| 749.5557_6.066 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.420586 | 10 | 0.0002930 | *** |
| 750.6771_10.977 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.278371 | 10 | 0.0459000 | * |
| 751.5717_6.812 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.887073 | 10 | 0.0162000 | * |
| 755.6054_6.838 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.183933 | 10 | 0.0004110 | *** |
| 756.5512_7.537 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.198349 | 10 | 0.0095200 | ** |
| 757.6221_7.776 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.959948 | 10 | 0.0143000 | * |
| 759.1094_6.983 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.648287 | 10 | 0.0244000 | * |
| 759.4259_6.982 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.281369 | 10 | 0.0016100 | ** |
| 759.6382_8.714 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.288984 | 10 | 0.0451000 | * |
| 760.3659_6.98 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.589948 | 10 | 0.0270000 | * |
| 760.421_6.983 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.554147 | 10 | 0.0287000 | * |
| 760.5329_6.983 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.560542 | 10 | 0.0051800 | ** |
| 760.5459_7.719 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.958440 | 10 | 0.0143000 | * |
| 760.5883_7.719 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.712476 | 10 | 0.0008260 | *** |
| 760.6287_6.982 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.976224 | 10 | 0.0139000 | * |
| 760.9763_7.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.502954 | 10 | 0.0057000 | ** |
| 761.0061_6.983 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.238070 | 10 | 0.0017200 | ** |
| 761.0763_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.992336 | 10 | 0.0135000 | * |
| 761.1927_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.539643 | 10 | 0.0053600 | ** |
| 761.2595_7.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.223183 | 10 | 0.0003880 | *** |
| 761.302_7.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.360447 | 10 | 0.0003190 | *** |
| 761.3174_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.078632 | 10 | 0.0004790 | *** |
| 761.329_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.217180 | 10 | 0.0000992 | **** |
| 761.3681_7.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.018328 | 10 | 0.0005230 | *** |
| 761.3781_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.794810 | 10 | 0.0007290 | *** |
| 761.4062_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.627743 | 10 | 0.0000587 | **** |
| 761.4141_7.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.649868 | 10 | 0.0002120 | *** |
| 761.4437_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 6.621500 | 10 | 0.0000592 | **** |
| 761.4545_7.719 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.358206 | 10 | 0.0003200 | *** |
| 761.5191_7.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.030493 | 10 | 0.0024000 | ** |
| 762.5032_7.719 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.259102 | 10 | 0.0085900 | ** |
| 762.5038_7.219 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.879223 | 10 | 0.0164000 | * |
| 762.5251_7.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.259952 | 10 | 0.0085700 | ** |
| 762.541_6.576 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.495720 | 10 | 0.0057700 | ** |
| 762.5433_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.368750 | 10 | 0.0394000 | * |
| 762.6465_8.683 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.350880 | 10 | 0.0406000 | * |
| 766.5356_8.411 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.549588 | 10 | 0.0289000 | * |
| 766.5755_7.417 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.199563 | 10 | 0.0095000 | ** |
| 768.551_7.206 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.495888 | 10 | 0.0317000 | * |
| 768.5536_6.434 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.480081 | 10 | 0.0325000 | * |
| 771.5762_7.712 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.255860 | 10 | 0.0016700 | ** |
| 771.6366_8.357 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.578324 | 10 | 0.0050300 | ** |
| 773.6516_9.025 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.329204 | 10 | 0.0076300 | ** |
| 775.6661_9.814 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.508378 | 10 | 0.0056500 | ** |
| 776.579_6.552 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.626572 | 10 | 0.0253000 | * |
| 780.5549_6.325 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.325567 | 10 | 0.0424000 | * |
| 780.5921_7.79 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.286691 | 10 | 0.0082000 | ** |
| 781.6192_8.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.601895 | 10 | 0.0264000 | * |
| 781.62_7.043 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.151504 | 10 | 0.0019800 | ** |
| 783.431_6.876 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.269323 | 10 | 0.0466000 | * |
| 783.6368_7.847 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.106310 | 10 | 0.0004600 | *** |
| 784.3942_6.875 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.257312 | 10 | 0.0476000 | * |
| 784.5042_6.876 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.228794 | 10 | 0.0499000 | * |
| 784.6654_10.38 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.009412 | 10 | 0.0024800 | ** |
| 785.6536_8.747 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.185869 | 10 | 0.0097200 | ** |
| 785.6542_8.953 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.264839 | 10 | 0.0085000 | ** |
| 786.998_8.053 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.412384 | 10 | 0.0365000 | * |
| 787.6674_9.202 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.021612 | 10 | 0.0129000 | * |
| 787.6701_9.896 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.748403 | 10 | 0.0037900 | ** |
| 788.5563_7.415 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.899328 | 10 | 0.0159000 | * |
| 788.6177_8.867 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.582580 | 10 | 0.0273000 | * |
| 790.5744_7.193 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.809908 | 10 | 0.0185000 | * |
| 791.5433_6.743 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.659951 | 10 | 0.0239000 | * |
| 792.5531_6.261 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.688357 | 10 | 0.0228000 | * |
| 792.5893_7.058 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.248018 | 10 | 0.0483000 | * |
| 796.5845_7.27 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.512298 | 10 | 0.0056100 | ** |
| 796.5848_7.423 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.862426 | 10 | 0.0169000 | * |
| 796.621_8.383 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.653310 | 10 | 0.0242000 | * |
| 797.6383_8.603 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.600854 | 10 | 0.0009790 | *** |
| 797.6421_8.39 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.800757 | 10 | 0.0188000 | * |
| 798.6518_8.391 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.073527 | 10 | 0.0004820 | *** |
| 798.681_10.534 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.335600 | 10 | 0.0014800 | ** |
| 799.6686_9.32 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.020639 | 10 | 0.0129000 | * |
| 799.6692_9.585 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.003415 | 10 | 0.0025000 | ** |
| 800.5726_6.827 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.403573 | 10 | 0.0067300 | ** |
| 801.686_10.409 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.793656 | 10 | 0.0190000 | * |
| 802.5357_6.323 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.295529 | 10 | 0.0446000 | * |
| 802.5717_7.81 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.456271 | 10 | 0.0061600 | ** |
| 803.037_6.321 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.425888 | 10 | 0.0357000 | * |
| 805.0108_3.238 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.626300 | 10 | 0.0046400 | ** |
| 805.6187_7.848 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.209535 | 10 | 0.0018000 | ** |
| 805.6784_9.291 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.656918 | 10 | 0.0240000 | * |
| 806.6235_6.835 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.473973 | 10 | 0.0329000 | * |
| 807.0682_7.274 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.439077 | 10 | 0.0349000 | * |
| 807.6349_8.948 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.291612 | 10 | 0.0081300 | ** |
| 807.6359_7.483 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.199096 | 10 | 0.0095100 | ** |
| 808.6375_8.751 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.140074 | 10 | 0.0105000 | * |
| 809.6507_9.876 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.694318 | 10 | 0.0041500 | ** |
| 809.6525_8.065 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.376990 | 10 | 0.0013800 | ** |
| 810.5988_8.865 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.293539 | 10 | 0.0447000 | * |
| 810.6041_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.312996 | 10 | 0.0433000 | * |
| 810.6564_8.03 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.195689 | 10 | 0.0018400 | ** |
| 811.2977_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.232538 | 10 | 0.0017400 | ** |
| 811.3445_7.959 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.769423 | 10 | 0.0198000 | * |
| 811.4149_7.961 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.200459 | 10 | 0.0018300 | ** |
| 811.5374_7.96 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.134960 | 10 | 0.0004410 | *** |
| 811.6292_7.819 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.652600 | 10 | 0.0242000 | * |
| 811.6703_8.961 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.860995 | 10 | 0.0169000 | * |
| 812.615_8.136 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.307248 | 10 | 0.0437000 | * |
| 812.6971_10.626 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.366738 | 10 | 0.0395000 | * |
| 813.6834_9.437 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.294580 | 10 | 0.0015700 | ** |
| 813.6854_10.226 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.982937 | 10 | 0.0137000 | * |
| 814.535_6.26 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.494118 | 10 | 0.0011500 | ** |
| 814.6127_7.96 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.593260 | 10 | 0.0049000 | ** |
| 815.7025_10.568 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.543498 | 10 | 0.0053300 | ** |
| 816.6467_9.969 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.887736 | 10 | 0.0162000 | * |
| 817.7051_10.344 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.522684 | 10 | 0.0302000 | * |
| 818.5668_7.42 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.442891 | 10 | 0.0347000 | * |
| 818.6049_7.793 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.737023 | 10 | 0.0007960 | *** |
| 819.6577_6.728 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.281530 | 10 | 0.0457000 | * |
| 820.5846_7.213 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.626086 | 10 | 0.0253000 | * |
| 820.6202_7.907 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.894889 | 10 | 0.0000422 | **** |
| 822.5994_7.509 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.824003 | 10 | 0.0180000 | * |
| 823.6664_10.409 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.399214 | 10 | 0.0067800 | ** |
| 824.616_8.544 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.965452 | 10 | 0.0005650 | *** |
| 824.6527_9.167 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.411620 | 10 | 0.0066400 | ** |
| 824.6537_9.596 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.426975 | 10 | 0.0356000 | * |
| 825.6241_9.886 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.891676 | 10 | 0.0161000 | * |
| 825.6778_9.575 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.083266 | 10 | 0.0004760 | *** |
| 825.6815_9.335 | rel_abund_log2 | b1 | b3 | 11 | 11 | -8.902253 | 10 | 0.0000046 | **** |
| 825.682_9.561 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.640444 | 10 | 0.0045300 | ** |
| 826.5348_5.993 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.550238 | 10 | 0.0052700 | ** |
| 826.5716_8.868 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.317315 | 10 | 0.0430000 | * |
| 827.6071_6.849 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.787202 | 10 | 0.0192000 | * |
| 827.663_8.483 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.240679 | 10 | 0.0489000 | * |
| 827.6998_10.277 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.529116 | 10 | 0.0010900 | ** |
| 827.6999_10.502 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.432917 | 10 | 0.0002880 | *** |
| 828.702_10.497 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.287469 | 10 | 0.0081800 | ** |
| 829.7155_10.657 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.604212 | 10 | 0.0048100 | ** |
| 831.57_7.208 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.387458 | 10 | 0.0381000 | * |
| 831.6353_8.196 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.504651 | 10 | 0.0002600 | *** |
| 832.582_7.631 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.325999 | 10 | 0.0423000 | * |
| 833.6507_8.971 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.880051 | 10 | 0.0164000 | * |
| 834.5932_5.658 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.097365 | 10 | 0.0113000 | * |
| 834.5933_5.658 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.293658 | 10 | 0.0081000 | ** |
| 834.6002_7.257 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.538562 | 10 | 0.0000657 | **** |
| 834.602_7.751 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.182630 | 10 | 0.0097800 | ** |
| 835.655_7.749 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.122365 | 10 | 0.0108000 | * |
| 835.6664_10.22 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.068750 | 10 | 0.0119000 | * |
| 835.6666_9.91 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.569816 | 10 | 0.0279000 | * |
| 837.6724_9.906 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.764084 | 10 | 0.0200000 | * |
| 837.6789_10.324 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.089231 | 10 | 0.0115000 | * |
| 837.6831_9.217 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.054443 | 10 | 0.0122000 | * |
| 838.6301_9.147 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.964374 | 10 | 0.0142000 | * |
| 844.6193_8.384 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.477625 | 10 | 0.0327000 | * |
| 846.6352_8.854 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.679591 | 10 | 0.0042500 | ** |
| 847.6893_7.716 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.725516 | 10 | 0.0039400 | ** |
| 848.6521_9.028 | rel_abund_log2 | b1 | b3 | 11 | 11 | -6.282026 | 10 | 0.0000912 | **** |
| 849.6682_6.746 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.658591 | 10 | 0.0240000 | * |
| 851.6839_7.537 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.769686 | 10 | 0.0198000 | * |
| 854.574_8.362 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.500453 | 10 | 0.0314000 | * |
| 855.6216_6.322 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.557677 | 10 | 0.0285000 | * |
| 856.5819_7.24 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.469703 | 10 | 0.0012000 | ** |
| 856.5823_7.778 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.396172 | 10 | 0.0068100 | ** |
| 858.5914_7.792 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.754446 | 10 | 0.0037600 | ** |
| 858.5977_8.453 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.149130 | 10 | 0.0103000 | * |
| 858.5992_7.06 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.822393 | 10 | 0.0181000 | * |
| 859.53_8.35 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.296554 | 10 | 0.0015700 | ** |
| 860.4936_6.313 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.811892 | 10 | 0.0034200 | ** |
| 861.7048_7.713 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.095519 | 10 | 0.0004670 | *** |
| 863.6838_8.874 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.821197 | 10 | 0.0181000 | * |
| 863.6839_6.733 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.536398 | 10 | 0.0295000 | * |
| 863.7092_11.343 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.606059 | 10 | 0.0262000 | * |
| 863.7199_8.448 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.913824 | 10 | 0.0155000 | * |
| 868.7389_11.19 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.279421 | 10 | 0.0458000 | * |
| 870.5225_6.318 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.323319 | 10 | 0.0077000 | ** |
| 870.6336_9.039 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.506489 | 10 | 0.0011300 | ** |
| 870.7558_11.296 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.521124 | 10 | 0.0303000 | * |
| 872.6526_8.477 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.811585 | 10 | 0.0184000 | * |
| 872.652_8.777 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.687152 | 10 | 0.0042000 | ** |
| 872.7731_11.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.376073 | 10 | 0.0389000 | * |
| 874.6655_10.042 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.985573 | 10 | 0.0137000 | * |
| 875.7095_11.292 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.451841 | 10 | 0.0342000 | * |
| 876.6835_10.245 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.999232 | 10 | 0.0005380 | *** |
| 877.6374_9.86 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.668621 | 10 | 0.0235000 | * |
| 877.7_7.713 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.319495 | 10 | 0.0077500 | ** |
| 878.7033_7.717 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.679006 | 10 | 0.0042500 | ** |
| 880.718_11.248 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.021233 | 10 | 0.0024300 | ** |
| 883.6892_7.419 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.759815 | 10 | 0.0201000 | * |
| 884.7697_11.358 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.456251 | 10 | 0.0339000 | * |
| 887.7206_8.05 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.288188 | 10 | 0.0452000 | * |
| 889.7358_8.865 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.986168 | 10 | 0.0137000 | * |
| 891.7156_7.718 | rel_abund_log2 | b1 | b3 | 11 | 11 | 4.878718 | 10 | 0.0006430 | *** |
| 891.7406_11.489 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.966121 | 10 | 0.0141000 | * |
| 894.7548_11.21 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.140741 | 10 | 0.0105000 | * |
| 896.5379_6.373 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.377184 | 10 | 0.0388000 | * |
| 896.7714_11.316 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.831107 | 10 | 0.0178000 | * |
| 897.7047_7.419 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.537577 | 10 | 0.0295000 | * |
| 898.7872_11.44 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.337460 | 10 | 0.0415000 | * |
| 899.7091_11.203 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.819009 | 10 | 0.0033800 | ** |
| 900.6836_9.96 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.090495 | 10 | 0.0114000 | * |
| 901.7251_11.318 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.927174 | 10 | 0.0151000 | * |
| 902.5743_7.805 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.921663 | 10 | 0.0028600 | ** |
| 902.7673_8.957 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.516477 | 10 | 0.0306000 | * |
| 903.6531_9.918 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.632260 | 10 | 0.0251000 | * |
| 903.7406_11.457 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.810060 | 10 | 0.0185000 | * |
| 904.7835_9.894 | rel_abund_log2 | b1 | b3 | 11 | 11 | 5.601563 | 10 | 0.0002270 | *** |
| 909.5461_8.55 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.525515 | 10 | 0.0301000 | * |
| 912.8727_11.58 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.774995 | 10 | 0.0196000 | * |
| 916.7378_11.078 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.575547 | 10 | 0.0276000 | * |
| 918.754_11.178 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.183675 | 10 | 0.0097600 | ** |
| 918.7987_9.897 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.246482 | 10 | 0.0485000 | * |
| 920.7705_11.302 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.875040 | 10 | 0.0030800 | ** |
| 922.7862_11.386 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.230804 | 10 | 0.0090100 | ** |
| 923.7092_11.174 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.209320 | 10 | 0.0093400 | ** |
| 928.7831_8.964 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.606576 | 10 | 0.0262000 | * |
| 930.7988_10.228 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.356292 | 10 | 0.0402000 | * |
| 931.7715_11.646 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.399047 | 10 | 0.0374000 | * |
| 938.8159_11.193 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.473442 | 10 | 0.0329000 | * |
| 941.7313_7.957 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.103900 | 10 | 0.0112000 | * |
| 942.7986_8.961 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.270771 | 10 | 0.0465000 | * |
| 942.849_11.418 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.936790 | 10 | 0.0149000 | * |
| 944.7698_11.221 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.279081 | 10 | 0.0459000 | * |
| 950.8156_11.512 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.548337 | 10 | 0.0289000 | * |
| 951.7153_7.77 | rel_abund_log2 | b1 | b3 | 11 | 11 | -4.999709 | 10 | 0.0005380 | *** |
| 963.8345_12.135 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.847882 | 10 | 0.0173000 | * |
| 965.7308_7.761 | rel_abund_log2 | b1 | b3 | 11 | 11 | -3.565726 | 10 | 0.0051300 | ** |
| 971.877_11.497 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.580789 | 10 | 0.0274000 | * |
| 972.8574_11.264 | rel_abund_log2 | b1 | b3 | 11 | 11 | 3.466533 | 10 | 0.0060600 | ** |
| 972.8_11.345 | rel_abund_log2 | b1 | b3 | 11 | 11 | -2.367070 | 10 | 0.0395000 | * |
| 993.7984_10.238 | rel_abund_log2 | b1 | b3 | 11 | 11 | -5.209554 | 10 | 0.0003960 | *** |
| 994.7158_9.773 | rel_abund_log2 | b1 | b3 | 11 | 11 | 2.229862 | 10 | 0.0499000 | * |
## [1] 438
# run paired t-tests for control intervention
beta_t.test_paired <- df_for_stats %>%
filter(treatment == "beta") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
beta_t.test_paired_sig <- beta_t.test_paired %>%
filter(p < 0.05)
kable(beta_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1002.658_3.079 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.907425 | 11 | 0.0143000 | * |
| 1014.9413_11.924 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.850869 | 11 | 0.0158000 | * |
| 1017.6873_3.095 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.490675 | 11 | 0.0300000 | * |
| 1028.9572_12.137 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.196432 | 11 | 0.0085100 | ** |
| 1034.9093_11.511 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.281384 | 11 | 0.0434000 | * |
| 1039.6688_3.106 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.090821 | 11 | 0.0103000 | * |
| 1041.6842_3.059 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.372954 | 11 | 0.0370000 | * |
| 1041.9353_11.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.141742 | 11 | 0.0093800 | ** |
| 1061.8543_10.506 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.252810 | 11 | 0.0013600 | ** |
| 1151.8656_7.71 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.467303 | 11 | 0.0313000 | * |
| 116.0707_0.623 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.044761 | 11 | 0.0112000 | * |
| 1163.3585_7.687 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.087624 | 11 | 0.0103000 | * |
| 1169.3422_6.843 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.612715 | 11 | 0.0241000 | * |
| 118.0864_0.597 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.508925 | 11 | 0.0048900 | ** |
| 1187.3609_7.137 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.189925 | 11 | 0.0086100 | ** |
| 1187.8642_7.137 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.389894 | 11 | 0.0359000 | * |
| 1192.8412_6.709 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.791911 | 11 | 0.0175000 | * |
| 1193.9116_8.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.545275 | 11 | 0.0272000 | * |
| 1198.856_7.141 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.517719 | 11 | 0.0286000 | * |
| 1202.3827_8.089 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.334710 | 11 | 0.0395000 | * |
| 1204.9038_8.864 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.549782 | 11 | 0.0270000 | * |
| 1204.9948_9.897 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.007036 | 11 | 0.0119000 | * |
| 1205.4054_8.863 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.127086 | 11 | 0.0096300 | ** |
| 1205.4975_9.898 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.786117 | 11 | 0.0177000 | * |
| 1216.4896_9.892 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.007214 | 11 | 0.0119000 | * |
| 1216.9911_9.896 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.342452 | 11 | 0.0011700 | ** |
| 1225.3783_7.964 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.876849 | 11 | 0.0151000 | * |
| 1229.9108_8.397 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.226932 | 11 | 0.0080600 | ** |
| 1229.9993_9.9 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.491862 | 11 | 0.0299000 | * |
| 1231.5117_9.905 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.287671 | 11 | 0.0072300 | ** |
| 1241.4043_8.395 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.932053 | 11 | 0.0136000 | * |
| 1242.5051_10.22 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.573750 | 11 | 0.0259000 | * |
| 1242.5053_9.906 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.280284 | 11 | 0.0073300 | ** |
| 1250.8236_3.081 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.456499 | 11 | 0.0319000 | * |
| 1258.8122_3.08 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.365428 | 11 | 0.0375000 | * |
| 1259.7951_10.087 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.532704 | 11 | 0.0278000 | * |
| 1263.8289_10.349 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.183898 | 11 | 0.0000687 | **** |
| 1266.8482_10.583 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.365921 | 11 | 0.0063000 | ** |
| 1268.1254_11.807 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.534893 | 11 | 0.0008510 | *** |
| 1268.2053_12.079 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.466816 | 11 | 0.0313000 | * |
| 1281.8397_10.519 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.821321 | 11 | 0.0166000 | * |
| 1289.1866_11.82 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.998637 | 11 | 0.0121000 | * |
| 1294.1419_11.81 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.602951 | 11 | 0.0246000 | * |
| 1297.1947_10.828 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.787689 | 11 | 0.0030100 | ** |
| 1298.1983_10.827 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.342600 | 11 | 0.0000550 | **** |
| 1302.2067_12.075 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.352610 | 11 | 0.0383000 | * |
| 1308.157_11.948 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.043360 | 11 | 0.0112000 | * |
| 1312.1724_11.668 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.539744 | 11 | 0.0275000 | * |
| 1316.1245_11.664 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.750386 | 11 | 0.0189000 | * |
| 1319.2346_12.075 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.566574 | 11 | 0.0262000 | * |
| 1322.2624_10.832 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.264459 | 11 | 0.0075400 | ** |
| 1334.1724_11.947 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.458028 | 11 | 0.0053500 | ** |
| 1339.2017_11.786 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.820542 | 11 | 0.0167000 | * |
| 1341.0272_6.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.014374 | 11 | 0.0118000 | * |
| 1341.2177_11.895 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.642532 | 11 | 0.0038700 | ** |
| 1342.2211_11.896 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.644123 | 11 | 0.0038600 | ** |
| 1346.1728_11.908 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.642554 | 11 | 0.0038700 | ** |
| 1347.1765_11.909 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.893544 | 11 | 0.0025000 | ** |
| 1364.2059_11.753 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.924465 | 11 | 0.0023700 | ** |
| 1365.0287_6.836 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.739490 | 11 | 0.0006100 | *** |
| 1369.1611_11.766 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.238957 | 11 | 0.0078900 | ** |
| 1389.0311_6.813 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.572890 | 11 | 0.0259000 | * |
| 1447.1208_6.507 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.281292 | 11 | 0.0073200 | ** |
| 1454.1867_9.904 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.668024 | 11 | 0.0219000 | * |
| 1454.1868_9.88 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.795578 | 11 | 0.0174000 | * |
| 1455.1905_9.914 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.656340 | 11 | 0.0223000 | * |
| 1470.0283_7.811 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.939774 | 11 | 0.0135000 | * |
| 1472.1218_6.472 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.692124 | 11 | 0.0209000 | * |
| 1473.1717_7.517 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.191936 | 11 | 0.0085800 | ** |
| 1475.1518_7.004 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.310182 | 11 | 0.0012300 | ** |
| 1476.1365_7.528 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.014291 | 11 | 0.0020400 | ** |
| 1476.1552_7.005 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.317983 | 11 | 0.0012200 | ** |
| 1478.1161_7.023 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.307626 | 11 | 0.0415000 | * |
| 1479.1185_7.024 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.825003 | 11 | 0.0001150 | *** |
| 1479.137_7.538 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.332142 | 11 | 0.0397000 | * |
| 1481.1402_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.999802 | 11 | 0.0121000 | * |
| 1482.1063_6.531 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.366914 | 11 | 0.0374000 | * |
| 1484.1047_7.781 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.550518 | 11 | 0.0270000 | * |
| 1487.1511_6.872 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.229099 | 11 | 0.0476000 | * |
| 1488.1014_8.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.657595 | 11 | 0.0223000 | * |
| 1488.1552_6.873 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.877212 | 11 | 0.0150000 | * |
| 1496.1206_6.463 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.382403 | 11 | 0.0363000 | * |
| 1497.1674_7.486 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.021558 | 11 | 0.0020100 | ** |
| 1500.0993_6.518 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.062807 | 11 | 0.0108000 | * |
| 1500.1349_7.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.358192 | 11 | 0.0002310 | *** |
| 1504.1822_7.513 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.275715 | 11 | 0.0439000 | * |
| 1506.1473_7.519 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.468064 | 11 | 0.0312000 | * |
| 1508.1474_7.516 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.918142 | 11 | 0.0024000 | ** |
| 1509.1474_7.507 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.500309 | 11 | 0.0009010 | *** |
| 1510.1192_7.922 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.754458 | 11 | 0.0187000 | * |
| 1510.1552_7.549 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.933067 | 11 | 0.0136000 | * |
| 1511.123_7.922 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.690336 | 11 | 0.0006600 | *** |
| 1514.1517_7.514 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.459244 | 11 | 0.0053400 | ** |
| 1518.2654_8.731 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.673586 | 11 | 0.0217000 | * |
| 152.0706_0.642 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.316651 | 11 | 0.0408000 | * |
| 1520.1651_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.093196 | 11 | 0.0102000 | * |
| 1524.1333_7.404 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.024948 | 11 | 0.0116000 | * |
| 1526.0908_8.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.585819 | 11 | 0.0042700 | ** |
| 1526.1136_6.481 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.138942 | 11 | 0.0094300 | ** |
| 1526.1519_7.532 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.610242 | 11 | 0.0242000 | * |
| 1527.094_8.977 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.278807 | 11 | 0.0013000 | ** |
| 1531.181_7.771 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.001248 | 11 | 0.0121000 | * |
| 1531.2124_8.09 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.380848 | 11 | 0.0011000 | ** |
| 1532.1012_7.882 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.694609 | 11 | 0.0209000 | * |
| 1532.1582_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.096079 | 11 | 0.0102000 | * |
| 1534.154_7.527 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.122997 | 11 | 0.0016900 | ** |
| 1534.1768_8.083 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.484478 | 11 | 0.0303000 | * |
| 1535.1558_7.533 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.341287 | 11 | 0.0391000 | * |
| 1536.6252_6.856 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.949118 | 11 | 0.0132000 | * |
| 1537.1733_7.573 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.363906 | 11 | 0.0376000 | * |
| 1538.1147_6.455 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.299004 | 11 | 0.0421000 | * |
| 1538.1514_7.472 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.099077 | 11 | 0.0017600 | ** |
| 1538.1799_7.62 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.515711 | 11 | 0.0287000 | * |
| 1540.1703_7.761 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.565141 | 11 | 0.0044300 | ** |
| 1541.1773_7.535 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.263644 | 11 | 0.0075500 | ** |
| 1542.1805_8.073 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.482580 | 11 | 0.0304000 | * |
| 1542.645_7.698 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.320922 | 11 | 0.0405000 | * |
| 1543.2104_7.94 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.561288 | 11 | 0.0044600 | ** |
| 1544.1542_7.691 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.447667 | 11 | 0.0324000 | * |
| 1544.1549_7.686 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.287555 | 11 | 0.0430000 | * |
| 1544.1615_7.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.691029 | 11 | 0.0210000 | * |
| 1546.1714_7.763 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.713602 | 11 | 0.0202000 | * |
| 1546.1749_7.78 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.126610 | 11 | 0.0096400 | ** |
| 1547.1793_7.789 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.479249 | 11 | 0.0306000 | * |
| 1547.1803_7.856 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.804606 | 11 | 0.0171000 | * |
| 1547.1806_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.279574 | 11 | 0.0436000 | * |
| 1548.0963_7.222 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.346615 | 11 | 0.0387000 | * |
| 1548.6599_7.695 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.214510 | 11 | 0.0488000 | * |
| 1550.1138_6.449 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.303956 | 11 | 0.0002510 | *** |
| 1550.1483_7.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.549974 | 11 | 0.0008300 | *** |
| 1554.1833_7.701 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.797293 | 11 | 0.0029600 | ** |
| 1555.3432_11.744 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.618689 | 11 | 0.0239000 | * |
| 1556.1613_7.323 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.232542 | 11 | 0.0473000 | * |
| 1558.1201_7.783 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.746015 | 11 | 0.0190000 | * |
| 1558.1745_7.924 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.705676 | 11 | 0.0204000 | * |
| 1561.1392_7.955 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.561201 | 11 | 0.0265000 | * |
| 1562.1488_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.744829 | 11 | 0.0006050 | *** |
| 1562.1493_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.667058 | 11 | 0.0006860 | *** |
| 1563.1535_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.262429 | 11 | 0.0000615 | **** |
| 1563.2185_8.966 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.841539 | 11 | 0.0027400 | ** |
| 1564.1347_6.888 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.322371 | 11 | 0.0404000 | * |
| 1564.1662_7.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.267267 | 11 | 0.0075000 | ** |
| 1566.1816_7.932 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.755427 | 11 | 0.0031800 | ** |
| 1567.1909_7.931 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.886102 | 11 | 0.0025400 | ** |
| 1568.6597_7.736 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.516232 | 11 | 0.0287000 | * |
| 1572.1304_7.394 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.879070 | 11 | 0.0025700 | ** |
| 1574.1454_7.378 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.879446 | 11 | 0.0150000 | * |
| 1574.2638_8.923 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.033478 | 11 | 0.0003820 | *** |
| 1574.6139_6.862 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.547225 | 11 | 0.0045700 | ** |
| 1578.1434_7.057 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.315252 | 11 | 0.0409000 | * |
| 1578.178_7.924 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.339597 | 11 | 0.0011800 | ** |
| 1579.649_7.139 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.657987 | 11 | 0.0223000 | * |
| 1579.6508_7.286 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.458319 | 11 | 0.0318000 | * |
| 1580.1032_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.074112 | 11 | 0.0106000 | * |
| 1580.159_7.285 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.208273 | 11 | 0.0083300 | ** |
| 1583.089_7.72 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.469750 | 11 | 0.0311000 | * |
| 1583.178_7.489 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.505236 | 11 | 0.0292000 | * |
| 1584.1871_8.061 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.629874 | 11 | 0.0234000 | * |
| 1585.2008_8.971 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.784666 | 11 | 0.0005670 | *** |
| 1586.2043_8.972 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.676389 | 11 | 0.0006760 | *** |
| 1589.2021_7.7 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.258924 | 11 | 0.0452000 | * |
| 1590.143_7.137 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.537882 | 11 | 0.0276000 | * |
| 1590.144_7.285 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.342018 | 11 | 0.0390000 | * |
| 1591.1476_7.288 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.285855 | 11 | 0.0431000 | * |
| 1591.1484_6.916 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.900142 | 11 | 0.0144000 | * |
| 1591.1826_7.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.174922 | 11 | 0.0015500 | ** |
| 1591.2114_7.764 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.990003 | 11 | 0.0123000 | * |
| 1592.1591_7.656 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.222332 | 11 | 0.0482000 | * |
| 1592.5935_6.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.549150 | 11 | 0.0270000 | * |
| 1593.0936_6.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.317764 | 11 | 0.0407000 | * |
| 1593.594_6.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.429335 | 11 | 0.0334000 | * |
| 1594.0976_6.869 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.457502 | 11 | 0.0318000 | * |
| 1596.3093_9.895 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.213292 | 11 | 0.0082600 | ** |
| 1597.3131_9.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.088396 | 11 | 0.0103000 | * |
| 1598.1451_7.197 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.980551 | 11 | 0.0125000 | * |
| 1604.1937_7.739 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.729686 | 11 | 0.0196000 | * |
| 1605.1979_7.674 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.537715 | 11 | 0.0046500 | ** |
| 1609.235_8.392 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.127931 | 11 | 0.0096100 | ** |
| 1610.32_9.892 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.517444 | 11 | 0.0001810 | *** |
| 1611.1156_6.852 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.209389 | 11 | 0.0493000 | * |
| 1611.8348_9.901 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.452773 | 11 | 0.0321000 | * |
| 1616.16_7.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.720610 | 11 | 0.0199000 | * |
| 1618.2939_8.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.881607 | 11 | 0.0149000 | * |
| 1619.2973_8.957 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.703208 | 11 | 0.0205000 | * |
| 1621.1991_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.994891 | 11 | 0.0122000 | * |
| 1621.2757_7.96 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.697107 | 11 | 0.0208000 | * |
| 1623.2097_7.994 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.504542 | 11 | 0.0293000 | * |
| 1624.3414_10.412 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.318307 | 11 | 0.0407000 | * |
| 1625.2172_8.052 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.371787 | 11 | 0.0370000 | * |
| 1631.6823_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.777338 | 11 | 0.0180000 | * |
| 1636.3352_9.901 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.307434 | 11 | 0.0415000 | * |
| 1637.3363_9.913 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.593149 | 11 | 0.0250000 | * |
| 1642.1751_7.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.851929 | 11 | 0.0157000 | * |
| 1646.2057_7.997 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.579937 | 11 | 0.0256000 | * |
| 1648.3413_9.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.622360 | 11 | 0.0237000 | * |
| 1649.3444_10.225 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.626105 | 11 | 0.0007330 | *** |
| 1649.3448_9.904 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.976151 | 11 | 0.0021700 | ** |
| 1677.4008_11.264 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.244614 | 11 | 0.0463000 | * |
| 1678.4082_11.291 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.572382 | 11 | 0.0259000 | * |
| 188.0707_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.908822 | 11 | 0.0142000 | * |
| 205.1221_0.621 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.428639 | 11 | 0.0056400 | ** |
| 235.0925_0.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.941466 | 11 | 0.0004420 | *** |
| 247.1441_0.636 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.727764 | 11 | 0.0197000 | * |
| 286.1437_1.13 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.875625 | 11 | 0.0001070 | *** |
| 286.2013_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.377728 | 11 | 0.0366000 | * |
| 308.1855_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | -11.533175 | 11 | 0.0000002 | **** |
| 312.1597_1.413 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.704638 | 11 | 0.0000093 | **** |
| 316.2483_0.679 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.066815 | 11 | 0.0107000 | * |
| 316.2608_0.679 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.574642 | 11 | 0.0007970 | *** |
| 330.2637_0.689 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.692511 | 11 | 0.0209000 | * |
| 331.0535_0.634 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.255705 | 11 | 0.0454000 | * |
| 332.243_0.64 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.333947 | 11 | 0.0066600 | ** |
| 337.1048_1.268 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.540238 | 11 | 0.0275000 | * |
| 342.2638_0.69 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.555970 | 11 | 0.0267000 | * |
| 344.2792_0.693 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.622212 | 11 | 0.0237000 | * |
| 344.2793_0.927 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.974884 | 11 | 0.0021800 | ** |
| 370.3549_12.075 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.218826 | 11 | 0.0485000 | * |
| 373.2483_1.147 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.718780 | 11 | 0.0001340 | *** |
| 428.3732_2.759 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.850698 | 11 | 0.0158000 | * |
| 438.2976_3.525 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.437815 | 11 | 0.0330000 | * |
| 464.1914_0.656 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.948715 | 11 | 0.0004370 | *** |
| 466.3289_4.123 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.152932 | 11 | 0.0016100 | ** |
| 480.3082_3.357 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.801752 | 11 | 0.0005520 | *** |
| 480.3446_3.381 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.568285 | 11 | 0.0261000 | * |
| 482.3238_2.701 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.979185 | 11 | 0.0021600 | ** |
| 502.2902_3.355 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.889509 | 11 | 0.0025200 | ** |
| 510.3551_3.445 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.509764 | 11 | 0.0048900 | ** |
| 518.322_3.062 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.460280 | 11 | 0.0317000 | * |
| 518.4564_5.944 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.496145 | 11 | 0.0297000 | * |
| 522.3564_3.24 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.468783 | 11 | 0.0052500 | ** |
| 526.2644_1.714 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.706981 | 11 | 0.0000010 | **** |
| 534.2952_3.067 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.237649 | 11 | 0.0469000 | * |
| 536.437_10.971 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.534995 | 11 | 0.0000115 | **** |
| 536.437_11.201 | rel_abund_log2 | b1 | b3 | 12 | 12 | -10.744586 | 11 | 0.0000004 | **** |
| 538.3859_4.051 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.109516 | 11 | 0.0017300 | ** |
| 544.3374_3.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.214357 | 11 | 0.0082400 | ** |
| 550.3862_3.859 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.347689 | 11 | 0.0386000 | * |
| 552.4018_4.301 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.639037 | 11 | 0.0230000 | * |
| 563.4268_4.262 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.695471 | 11 | 0.0208000 | * |
| 563.4273_4.415 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.662361 | 11 | 0.0037400 | ** |
| 577.5185_8.358 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.649623 | 11 | 0.0007060 | *** |
| 593.4757_4.54 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.662733 | 11 | 0.0001460 | *** |
| 593.4762_4.644 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.087222 | 11 | 0.0003510 | *** |
| 593.4768_4.312 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.871921 | 11 | 0.0152000 | * |
| 595.4929_4.645 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.671726 | 11 | 0.0001440 | *** |
| 596.4154_4.906 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.941480 | 11 | 0.0000008 | **** |
| 597.5086_5.242 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.764102 | 11 | 0.0184000 | * |
| 605.5497_9.629 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.676363 | 11 | 0.0001430 | *** |
| 608.4637_5.649 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.599679 | 11 | 0.0247000 | * |
| 610.431_5.16 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.115381 | 11 | 0.0003360 | *** |
| 617.4748_4.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.692115 | 11 | 0.0035500 | ** |
| 619.4903_4.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | 10.205135 | 11 | 0.0000006 | **** |
| 620.5974_10.522 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.526577 | 11 | 0.0047400 | ** |
| 622.6132_10.675 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.309903 | 11 | 0.0069500 | ** |
| 629.1522_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.743936 | 11 | 0.0191000 | * |
| 634.5399_9.061 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.845417 | 11 | 0.0159000 | * |
| 634.54_8.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.213168 | 11 | 0.0014500 | ** |
| 642.6107_12.077 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.949128 | 11 | 0.0132000 | * |
| 643.38_0.69 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.215580 | 11 | 0.0082200 | ** |
| 650.6449_10.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.716463 | 11 | 0.0006330 | *** |
| 652.6596_10.839 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.249561 | 11 | 0.0013700 | ** |
| 661.5282_5.288 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.580737 | 11 | 0.0043100 | ** |
| 664.6011_11.668 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.993949 | 11 | 0.0021100 | ** |
| 672.3751_3.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.692089 | 11 | 0.0210000 | * |
| 673.5278_5.092 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.395664 | 11 | 0.0355000 | * |
| 673.5905_11.756 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.864084 | 11 | 0.0026300 | ** |
| 677.5582_5.973 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.691172 | 11 | 0.0035600 | ** |
| 677.5586_5.973 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.468487 | 11 | 0.0052500 | ** |
| 690.6188_11.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.145889 | 11 | 0.0016300 | ** |
| 691.5742_6.403 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.237647 | 11 | 0.0079100 | ** |
| 692.5581_7.191 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.368246 | 11 | 0.0373000 | * |
| 692.6335_11.899 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.359966 | 11 | 0.0011400 | ** |
| 693.6368_11.899 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.471562 | 11 | 0.0009440 | *** |
| 695.5093_5.094 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.729085 | 11 | 0.0196000 | * |
| 695.5729_11.772 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.680719 | 11 | 0.0036200 | ** |
| 700.5274_7.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.566365 | 11 | 0.0044200 | ** |
| 717.5903_6.828 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.320846 | 11 | 0.0068200 | ** |
| 717.5903_7.018 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.789410 | 11 | 0.0005630 | *** |
| 718.5742_8.138 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.892058 | 11 | 0.0147000 | * |
| 718.5934_7.015 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.085874 | 11 | 0.0018000 | ** |
| 719.6006_7.392 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.116536 | 11 | 0.0098100 | ** |
| 720.5532_7.039 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.420449 | 11 | 0.0057200 | ** |
| 721.5835_6.066 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.359152 | 11 | 0.0379000 | * |
| 721.5846_6.067 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.592012 | 11 | 0.0042300 | ** |
| 724.5277_7.806 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.031028 | 11 | 0.0114000 | * |
| 726.5428_8.237 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.335314 | 11 | 0.0000556 | **** |
| 727.5745_6.067 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.898839 | 11 | 0.0145000 | * |
| 728.5588_9.081 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.677997 | 11 | 0.0215000 | * |
| 732.589_8.53 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.553212 | 11 | 0.0001720 | *** |
| 733.6212_7.929 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.021619 | 11 | 0.0116000 | * |
| 733.6215_7.929 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.426096 | 11 | 0.0056600 | ** |
| 738.543_8.181 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.159909 | 11 | 0.0015900 | ** |
| 740.5626_6.841 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.392254 | 11 | 0.0357000 | * |
| 740.5662_6.833 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.412049 | 11 | 0.0058000 | ** |
| 742.5377_7.462 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.323889 | 11 | 0.0403000 | * |
| 742.5718_8.28 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.627766 | 11 | 0.0007310 | *** |
| 742.5753_7.523 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.791593 | 11 | 0.0029900 | ** |
| 743.6054_7.227 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.501344 | 11 | 0.0008990 | *** |
| 744.5538_6.51 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.834486 | 11 | 0.0162000 | * |
| 745.6202_7.852 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.131288 | 11 | 0.0016700 | ** |
| 745.6214_8.115 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.618958 | 11 | 0.0040300 | ** |
| 746.5091_7.814 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.915875 | 11 | 0.0140000 | * |
| 746.5685_9.239 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.731171 | 11 | 0.0006180 | *** |
| 746.6055_8.435 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.295541 | 11 | 0.0424000 | * |
| 748.5258_8.233 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.230601 | 11 | 0.0014100 | ** |
| 748.5844_7.89 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.820936 | 11 | 0.0166000 | * |
| 748.5844_8.099 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.443001 | 11 | 0.0326000 | * |
| 749.5557_6.066 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.862613 | 11 | 0.0154000 | * |
| 750.7353_10.695 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.258322 | 11 | 0.0452000 | * |
| 752.5592_8.969 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.583419 | 11 | 0.0254000 | * |
| 752.5594_8.973 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.162236 | 11 | 0.0015800 | ** |
| 754.5373_6.002 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.531715 | 11 | 0.0279000 | * |
| 755.4833_5.662 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.759038 | 11 | 0.0031600 | ** |
| 755.6054_6.838 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.565031 | 11 | 0.0044300 | ** |
| 756.5904_7.889 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.104934 | 11 | 0.0017400 | ** |
| 758.9871_6.983 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.725629 | 11 | 0.0033500 | ** |
| 759.0903_6.983 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.428122 | 11 | 0.0335000 | * |
| 759.1221_6.983 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.281666 | 11 | 0.0434000 | * |
| 759.6382_8.714 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.723708 | 11 | 0.0198000 | * |
| 760.4052_6.983 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.945009 | 11 | 0.0133000 | * |
| 760.5883_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.494677 | 11 | 0.0050200 | ** |
| 761.1927_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.488159 | 11 | 0.0301000 | * |
| 761.2595_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.589685 | 11 | 0.0252000 | * |
| 761.302_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.769744 | 11 | 0.0182000 | * |
| 761.3174_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.759162 | 11 | 0.0031600 | ** |
| 761.329_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.777150 | 11 | 0.0180000 | * |
| 761.3681_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.246429 | 11 | 0.0077800 | ** |
| 761.3781_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.463379 | 11 | 0.0315000 | * |
| 761.4062_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.577822 | 11 | 0.0257000 | * |
| 761.4545_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.883713 | 11 | 0.0149000 | * |
| 761.5191_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.840790 | 11 | 0.0161000 | * |
| 761.6526_9.142 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.341413 | 11 | 0.0391000 | * |
| 762.5032_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.058724 | 11 | 0.0109000 | * |
| 762.5251_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.502095 | 11 | 0.0294000 | * |
| 762.5433_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.159319 | 11 | 0.0090900 | ** |
| 764.5195_7.458 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.488784 | 11 | 0.0050700 | ** |
| 766.5755_7.417 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.948182 | 11 | 0.0004370 | *** |
| 768.5536_6.434 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.877241 | 11 | 0.0004890 | *** |
| 768.587_8.451 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.669638 | 11 | 0.0036900 | ** |
| 770.0615_7.009 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.393102 | 11 | 0.0357000 | * |
| 770.5691_6.825 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.461206 | 11 | 0.0009610 | *** |
| 770.6056_8.664 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.449796 | 11 | 0.0054300 | ** |
| 771.5762_7.712 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.550190 | 11 | 0.0270000 | * |
| 771.6366_8.357 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.850766 | 11 | 0.0158000 | * |
| 772.5855_7.501 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.816455 | 11 | 0.0168000 | * |
| 772.6196_8.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.454018 | 11 | 0.0002000 | *** |
| 773.6516_9.025 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.477147 | 11 | 0.0001930 | *** |
| 774.5401_8.626 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.651177 | 11 | 0.0225000 | * |
| 774.5404_8.982 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.436479 | 11 | 0.0055600 | ** |
| 775.6384_6.825 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.134161 | 11 | 0.0095100 | ** |
| 775.6661_9.814 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.633065 | 11 | 0.0001530 | *** |
| 778.5376_5.842 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.247271 | 11 | 0.0461000 | * |
| 780.5519_6.84 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.252107 | 11 | 0.0457000 | * |
| 780.5795_10.219 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.855147 | 11 | 0.0157000 | * |
| 780.5921_7.79 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.826367 | 11 | 0.0005300 | *** |
| 781.6192_8.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.578271 | 11 | 0.0257000 | * |
| 781.62_7.043 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.769563 | 11 | 0.0031000 | ** |
| 783.3522_6.877 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.555880 | 11 | 0.0267000 | * |
| 783.4174_6.877 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.422076 | 11 | 0.0339000 | * |
| 783.5024_6.875 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.262701 | 11 | 0.0449000 | * |
| 783.6349_9.156 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.240186 | 11 | 0.0467000 | * |
| 783.6368_7.847 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.044902 | 11 | 0.0019300 | ** |
| 784.5879_7.141 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.769828 | 11 | 0.0182000 | * |
| 784.5881_7.29 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.281226 | 11 | 0.0434000 | * |
| 784.6654_10.38 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.297234 | 11 | 0.0000586 | **** |
| 785.452_7.291 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.370014 | 11 | 0.0372000 | * |
| 785.5792_6.874 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.419479 | 11 | 0.0340000 | * |
| 785.6536_8.747 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.288490 | 11 | 0.0072200 | ** |
| 785.6542_8.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.521950 | 11 | 0.0008690 | *** |
| 786.6508_7.289 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.405576 | 11 | 0.0349000 | * |
| 787.6674_9.202 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.894262 | 11 | 0.0001040 | *** |
| 787.6701_9.896 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.541030 | 11 | 0.0046200 | ** |
| 788.5563_7.415 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.337840 | 11 | 0.0066200 | ** |
| 788.6177_8.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.592324 | 11 | 0.0250000 | * |
| 789.0076_8.052 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.984137 | 11 | 0.0124000 | * |
| 790.5352_6.424 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.450170 | 11 | 0.0322000 | * |
| 790.5744_7.193 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.919270 | 11 | 0.0140000 | * |
| 792.5512_6.827 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.131721 | 11 | 0.0095500 | ** |
| 792.5531_6.261 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.808658 | 11 | 0.0170000 | * |
| 792.5873_8.665 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.479650 | 11 | 0.0306000 | * |
| 792.5893_7.058 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.285182 | 11 | 0.0431000 | * |
| 793.556_6.829 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.329566 | 11 | 0.0399000 | * |
| 794.0604_7.102 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.287932 | 11 | 0.0429000 | * |
| 794.5862_7.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.071435 | 11 | 0.0106000 | * |
| 794.6054_8.214 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.235669 | 11 | 0.0471000 | * |
| 794.6057_8.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.364109 | 11 | 0.0375000 | * |
| 796.5222_9.007 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.128602 | 11 | 0.0096000 | ** |
| 796.5247_6.843 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.341802 | 11 | 0.0390000 | * |
| 796.5845_7.27 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.313980 | 11 | 0.0069000 | ** |
| 796.5848_7.423 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.714368 | 11 | 0.0006350 | *** |
| 797.6383_8.603 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.253934 | 11 | 0.0456000 | * |
| 797.6421_8.39 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.407024 | 11 | 0.0010500 | ** |
| 797.6453_8.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.383163 | 11 | 0.0061100 | ** |
| 798.6003_7.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.985374 | 11 | 0.0004120 | *** |
| 798.635_8.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.305605 | 11 | 0.0416000 | * |
| 798.6518_8.391 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.437323 | 11 | 0.0009990 | *** |
| 798.681_10.534 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.915145 | 11 | 0.0141000 | * |
| 799.6069_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.780380 | 11 | 0.0179000 | * |
| 799.6686_9.32 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.367844 | 11 | 0.0011200 | ** |
| 800.6163_8.637 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.975289 | 11 | 0.0126000 | * |
| 800.616_8.426 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.496872 | 11 | 0.0297000 | * |
| 801.686_10.409 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.318134 | 11 | 0.0407000 | * |
| 802.5715_9.823 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.409173 | 11 | 0.0347000 | * |
| 802.5716_10.204 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.503675 | 11 | 0.0293000 | * |
| 802.5717_7.81 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.983925 | 11 | 0.0124000 | * |
| 804.5937_7.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.852410 | 11 | 0.0157000 | * |
| 805.0108_3.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.694286 | 11 | 0.0209000 | * |
| 805.6187_7.848 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.491854 | 11 | 0.0050400 | ** |
| 806.5698_6.37 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.798193 | 11 | 0.0005550 | *** |
| 806.6235_6.835 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.375917 | 11 | 0.0368000 | * |
| 807.6349_8.948 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.533365 | 11 | 0.0001770 | *** |
| 807.6359_7.483 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.802417 | 11 | 0.0172000 | * |
| 808.6375_8.751 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.062717 | 11 | 0.0003650 | *** |
| 809.6507_9.876 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.451535 | 11 | 0.0009760 | *** |
| 809.6525_8.065 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.513772 | 11 | 0.0008810 | *** |
| 810.5875_6.727 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.646499 | 11 | 0.0227000 | * |
| 810.5988_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.859012 | 11 | 0.0155000 | * |
| 810.6013_7.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.244974 | 11 | 0.0078000 | ** |
| 810.6041_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.578837 | 11 | 0.0256000 | * |
| 810.6564_8.03 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.410515 | 11 | 0.0010400 | ** |
| 810.6812_10.387 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.221920 | 11 | 0.0482000 | * |
| 811.0052_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.375343 | 11 | 0.0368000 | * |
| 811.2977_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.939843 | 11 | 0.0023100 | ** |
| 811.3445_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.330306 | 11 | 0.0398000 | * |
| 811.6292_7.819 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.459812 | 11 | 0.0009630 | *** |
| 811.6598_8.009 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.694497 | 11 | 0.0209000 | * |
| 811.6703_8.961 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.739847 | 11 | 0.0192000 | * |
| 812.545_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.658052 | 11 | 0.0223000 | * |
| 812.5561_7.198 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.416942 | 11 | 0.0342000 | * |
| 812.615_8.136 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.723360 | 11 | 0.0198000 | * |
| 812.6186_8.396 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.321182 | 11 | 0.0405000 | * |
| 812.6971_10.626 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.745936 | 11 | 0.0190000 | * |
| 813.6106_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.439900 | 11 | 0.0328000 | * |
| 813.6834_9.437 | rel_abund_log2 | b1 | b3 | 12 | 12 | -8.260860 | 11 | 0.0000048 | **** |
| 813.6854_10.226 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.605534 | 11 | 0.0007580 | *** |
| 813.6866_9.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.307537 | 11 | 0.0069800 | ** |
| 814.6318_9.087 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.237745 | 11 | 0.0469000 | * |
| 815.5406_6.313 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.920939 | 11 | 0.0139000 | * |
| 815.6606_9.084 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.539585 | 11 | 0.0275000 | * |
| 815.7025_10.568 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.078741 | 11 | 0.0018200 | ** |
| 816.5873_8.216 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.281420 | 11 | 0.0073200 | ** |
| 816.7069_11.125 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.221497 | 11 | 0.0482000 | * |
| 817.7051_10.344 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.759460 | 11 | 0.0001270 | *** |
| 818.5668_7.42 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.105735 | 11 | 0.0100000 | ** |
| 818.6044_8.299 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.408189 | 11 | 0.0347000 | * |
| 819.5737_7.769 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.234054 | 11 | 0.0472000 | * |
| 820.5846_7.213 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.579088 | 11 | 0.0043300 | ** |
| 820.6961_11.574 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.220326 | 11 | 0.0081500 | ** |
| 821.5898_7.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.442463 | 11 | 0.0327000 | * |
| 822.5404_7.148 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.833266 | 11 | 0.0163000 | * |
| 822.5994_7.509 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.516004 | 11 | 0.0048300 | ** |
| 824.616_8.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.732345 | 11 | 0.0033100 | ** |
| 824.6526_9.963 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.305562 | 11 | 0.0416000 | * |
| 825.6241_9.886 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.813073 | 11 | 0.0169000 | * |
| 825.6473_7.81 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.795024 | 11 | 0.0174000 | * |
| 825.6815_9.335 | rel_abund_log2 | b1 | b3 | 12 | 12 | -8.248568 | 11 | 0.0000049 | **** |
| 825.682_9.561 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.896459 | 11 | 0.0024900 | ** |
| 827.663_8.483 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.566489 | 11 | 0.0008080 | *** |
| 827.6998_10.277 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.180185 | 11 | 0.0000691 | **** |
| 827.6999_10.389 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.387168 | 11 | 0.0060700 | ** |
| 827.6999_10.502 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.084454 | 11 | 0.0018100 | ** |
| 828.5511_6.373 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.255520 | 11 | 0.0013500 | ** |
| 828.702_10.497 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.291898 | 11 | 0.0012700 | ** |
| 829.6782_8.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.647517 | 11 | 0.0038400 | ** |
| 829.6788_9.393 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.244055 | 11 | 0.0464000 | * |
| 829.7155_10.657 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.498199 | 11 | 0.0009040 | *** |
| 830.568_6.228 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.650116 | 11 | 0.0226000 | * |
| 831.6353_8.196 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.567245 | 11 | 0.0001680 | *** |
| 831.6573_8.408 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.355279 | 11 | 0.0064200 | ** |
| 831.694_9.318 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.990044 | 11 | 0.0123000 | * |
| 832.5817_7.451 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.069581 | 11 | 0.0107000 | * |
| 832.582_7.631 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.256063 | 11 | 0.0002700 | *** |
| 832.7384_11.319 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.299725 | 11 | 0.0421000 | * |
| 833.6507_8.971 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.637073 | 11 | 0.0231000 | * |
| 834.5987_8.394 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.247121 | 11 | 0.0077700 | ** |
| 834.6002_7.257 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.533074 | 11 | 0.0008540 | *** |
| 835.6566_8.968 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.562955 | 11 | 0.0264000 | * |
| 835.6663_8.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.903886 | 11 | 0.0143000 | * |
| 835.6664_10.22 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.437028 | 11 | 0.0002050 | *** |
| 835.6666_9.91 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.738576 | 11 | 0.0032700 | ** |
| 836.6136_9.086 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.461375 | 11 | 0.0316000 | * |
| 836.6141_8.438 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.271088 | 11 | 0.0442000 | * |
| 837.5477_8.363 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.077289 | 11 | 0.0000799 | **** |
| 837.614_7.764 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.776988 | 11 | 0.0180000 | * |
| 837.6789_10.324 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.164589 | 11 | 0.0090100 | ** |
| 837.6818_10.567 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.477574 | 11 | 0.0307000 | * |
| 837.6831_9.217 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.910826 | 11 | 0.0004640 | *** |
| 838.6301_9.147 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.159909 | 11 | 0.0090800 | ** |
| 838.6321_8.779 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.703999 | 11 | 0.0205000 | * |
| 839.6989_10.178 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.027445 | 11 | 0.0115000 | * |
| 842.5663_7.213 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.157533 | 11 | 0.0091200 | ** |
| 842.723_11.155 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.571412 | 11 | 0.0043800 | ** |
| 844.7397_11.265 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.024480 | 11 | 0.0116000 | * |
| 847.6893_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.688455 | 11 | 0.0211000 | * |
| 848.6521_9.028 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.444575 | 11 | 0.0000478 | **** |
| 849.6939_11.262 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.713895 | 11 | 0.0202000 | * |
| 850.5715_8.394 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.530458 | 11 | 0.0280000 | * |
| 851.64_9.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.264642 | 11 | 0.0075400 | ** |
| 852.6843_10.599 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.865604 | 11 | 0.0154000 | * |
| 853.6758_9.321 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.186738 | 11 | 0.0086600 | ** |
| 853.6786_8.974 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.872336 | 11 | 0.0026000 | ** |
| 853.6786_8.975 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.733762 | 11 | 0.0194000 | * |
| 854.574_8.362 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.744565 | 11 | 0.0191000 | * |
| 856.5819_7.24 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.635507 | 11 | 0.0232000 | * |
| 856.5823_7.778 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.662913 | 11 | 0.0221000 | * |
| 858.5914_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.971030 | 11 | 0.0127000 | * |
| 858.5977_8.453 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.535463 | 11 | 0.0277000 | * |
| 858.5992_7.06 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.434204 | 11 | 0.0332000 | * |
| 858.7545_11.345 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.026376 | 11 | 0.0019900 | ** |
| 859.53_8.35 | rel_abund_log2 | b1 | b3 | 12 | 12 | 8.076764 | 11 | 0.0000060 | **** |
| 859.6891_7.519 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.565367 | 11 | 0.0263000 | * |
| 860.6131_9.152 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.574059 | 11 | 0.0259000 | * |
| 860.6133_8.785 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.529482 | 11 | 0.0047200 | ** |
| 861.7048_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.431063 | 11 | 0.0333000 | * |
| 863.7199_8.448 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.266135 | 11 | 0.0446000 | * |
| 865.5795_9.626 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.369271 | 11 | 0.0000141 | **** |
| 868.7389_11.19 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.608625 | 11 | 0.0041100 | ** |
| 870.6336_9.039 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.821599 | 11 | 0.0166000 | * |
| 870.7558_11.296 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.664656 | 11 | 0.0037200 | ** |
| 871.6776_11.111 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.393305 | 11 | 0.0357000 | * |
| 871.6892_7.141 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.817138 | 11 | 0.0168000 | * |
| 872.6526_8.477 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.430828 | 11 | 0.0334000 | * |
| 872.652_8.777 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.665764 | 11 | 0.0006870 | *** |
| 872.7324_11.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.066165 | 11 | 0.0107000 | * |
| 872.7731_11.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.210925 | 11 | 0.0082900 | ** |
| 873.7045_7.52 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.350664 | 11 | 0.0011500 | ** |
| 875.7095_11.292 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.358804 | 11 | 0.0011400 | ** |
| 875.7207_8.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.710166 | 11 | 0.0203000 | * |
| 876.5695_7.88 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.251274 | 11 | 0.0002720 | *** |
| 876.6835_10.245 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.923586 | 11 | 0.0023800 | ** |
| 877.6374_9.86 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.355077 | 11 | 0.0064200 | ** |
| 877.6374_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.297251 | 11 | 0.0422000 | * |
| 877.7253_11.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.829882 | 11 | 0.0164000 | * |
| 877.7_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.139553 | 11 | 0.0094200 | ** |
| 878.5852_8.857 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.823743 | 11 | 0.0166000 | * |
| 880.718_11.248 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.720664 | 11 | 0.0033800 | ** |
| 883.6892_7.419 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.011250 | 11 | 0.0003950 | *** |
| 883.6894_6.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.429346 | 11 | 0.0334000 | * |
| 884.7697_11.358 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.602529 | 11 | 0.0246000 | * |
| 886.7858_11.492 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.573929 | 11 | 0.0259000 | * |
| 887.7201_8.662 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.553891 | 11 | 0.0268000 | * |
| 889.6375_9.586 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.152710 | 11 | 0.0016100 | ** |
| 889.7358_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.622630 | 11 | 0.0040100 | ** |
| 891.7156_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.354040 | 11 | 0.0382000 | * |
| 893.7563_11.645 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.038812 | 11 | 0.0113000 | * |
| 894.7548_11.21 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.829323 | 11 | 0.0164000 | * |
| 896.5379_6.373 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.011363 | 11 | 0.0118000 | * |
| 896.7714_11.316 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.859154 | 11 | 0.0155000 | * |
| 897.7047_7.419 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.460648 | 11 | 0.0000126 | **** |
| 897.7049_6.893 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.303532 | 11 | 0.0418000 | * |
| 899.684_6.44 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.311324 | 11 | 0.0412000 | * |
| 899.7091_11.203 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.253770 | 11 | 0.0076800 | ** |
| 899.7208_8.396 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.223400 | 11 | 0.0481000 | * |
| 901.6999_7.143 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.770105 | 11 | 0.0182000 | * |
| 901.7251_11.318 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.593387 | 11 | 0.0250000 | * |
| 902.7673_8.957 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.611847 | 11 | 0.0040800 | ** |
| 902.8182_11.738 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.192206 | 11 | 0.0002980 | *** |
| 903.6531_10.207 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.061865 | 11 | 0.0108000 | * |
| 903.6531_9.918 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.246741 | 11 | 0.0077800 | ** |
| 904.7835_9.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.454560 | 11 | 0.0320000 | * |
| 904.8336_11.91 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.348976 | 11 | 0.0011600 | ** |
| 907.7722_11.767 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.326090 | 11 | 0.0401000 | * |
| 911.7206_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.392166 | 11 | 0.0357000 | * |
| 912.8727_11.58 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.321919 | 11 | 0.0068100 | ** |
| 913.7362_8.395 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.229213 | 11 | 0.0476000 | * |
| 915.7154_7.143 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.229691 | 11 | 0.0080200 | ** |
| 918.754_11.178 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.684068 | 11 | 0.0213000 | * |
| 918.7987_9.897 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.778028 | 11 | 0.0001230 | *** |
| 919.7465_8.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.257752 | 11 | 0.0453000 | * |
| 923.684_6.374 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.239548 | 11 | 0.0078800 | ** |
| 928.7831_8.964 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.273935 | 11 | 0.0440000 | * |
| 928.8306_11.753 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.796540 | 11 | 0.0174000 | * |
| 930.7988_10.228 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.972592 | 11 | 0.0004200 | *** |
| 930.7989_9.903 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.423251 | 11 | 0.0056900 | ** |
| 930.8479_11.919 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.937493 | 11 | 0.0023200 | ** |
| 937.6995_6.371 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.661694 | 11 | 0.0037400 | ** |
| 938.8159_11.193 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.235770 | 11 | 0.0079300 | ** |
| 941.7308_7.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.566482 | 11 | 0.0262000 | * |
| 941.7313_7.957 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.581726 | 11 | 0.0255000 | * |
| 944.7698_11.221 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.783268 | 11 | 0.0178000 | * |
| 944.8145_9.903 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.091203 | 11 | 0.0103000 | * |
| 949.7248_11.221 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.836895 | 11 | 0.0162000 | * |
| 951.7153_7.77 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.851466 | 11 | 0.0158000 | * |
| 953.7558_11.474 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.569263 | 11 | 0.0261000 | * |
| 955.7465_8.781 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.674839 | 11 | 0.0216000 | * |
| 963.8345_12.135 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.398651 | 11 | 0.0353000 | * |
| 965.7308_7.761 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.844707 | 11 | 0.0159000 | * |
| 966.8463_11.305 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.715736 | 11 | 0.0201000 | * |
| 972.7339_9.773 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.024516 | 11 | 0.0116000 | * |
| 974.75_10.504 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.903641 | 11 | 0.0144000 | * |
| 984.86_11.28 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.263426 | 11 | 0.0448000 | * |
| 993.7984_10.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.136536 | 11 | 0.0094700 | ** |
## [1] 592
# let's grab both datasets for features sig in pre v post beta and pre v post control and combine them
fulljoin_t.test_betaANDctrl <- full_join(beta_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".beta", ".ctrl"))
# number of features in combined list
nrow(fulljoin_t.test_betaANDctrl)## [1] 778
Let’s try and remove features significant due to the background diet
sig_paired_beta_rmBG <- fulljoin_t.test_betaANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.beta * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features without bg diet effect
nrow(sig_paired_beta_rmBG)## [1] 342
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 250
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
beta_sig_ANOVA_overlap_paired <- inner_join(sig_paired_beta_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(beta_sig_ANOVA_overlap_paired$mz_rt)## [1] "1341.2177_11.895" "1342.2211_11.896" "1479.1185_7.024" "1526.0908_8.976"
## [5] "1527.094_8.977" "1536.6252_6.856" "1594.0976_6.869" "1618.2939_8.956"
## [9] "1619.2973_8.957" "1649.3448_9.904" "286.2013_0.633" "337.1048_1.268"
## [13] "536.437_11.201" "605.5497_9.629" "608.4637_5.649" "677.5582_5.973"
## [17] "677.5586_5.973" "726.5428_8.237" "738.543_8.181" "748.5258_8.233"
## [21] "755.4833_5.662" "759.0903_6.983" "759.1221_6.983" "774.5404_8.982"
## [25] "796.5222_9.007" "797.6453_8.401" "811.6598_8.009" "813.6866_9.902"
## [29] "827.6999_10.389" "835.6566_8.968" "851.64_9.902" "865.5795_9.626"
## [33] "876.5695_7.88" "878.5852_8.857" "889.6375_9.586" "903.6531_10.207"
## [37] "907.7722_11.767" "974.75_10.504"
# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
filter(treatment == "red") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
filter(p < 0.05)
kable(red_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 100.0757_0.629 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.301791 | 11 | 0.0419000 | * |
| 1017.6873_3.095 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.890974 | 11 | 0.0147000 | * |
| 1020.8931_11.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.210565 | 11 | 0.0492000 | * |
| 1022.9086_11.654 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.378913 | 11 | 0.0366000 | * |
| 1026.6606_3.098 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.958742 | 11 | 0.0130000 | * |
| 1034.9093_11.511 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.505686 | 11 | 0.0292000 | * |
| 1036.925_11.654 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.164878 | 11 | 0.0003110 | *** |
| 1039.6688_3.106 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.746647 | 11 | 0.0190000 | * |
| 1041.9353_11.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.390033 | 11 | 0.0060300 | ** |
| 1128.671_2.722 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.292666 | 11 | 0.0426000 | * |
| 1151.3639_7.711 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.321264 | 11 | 0.0012100 | ** |
| 1151.8656_7.71 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.583845 | 11 | 0.0000395 | **** |
| 1172.8404_7.074 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.445031 | 11 | 0.0325000 | * |
| 118.0864_0.597 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.527861 | 11 | 0.0000116 | **** |
| 1193.9116_8.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.062703 | 11 | 0.0108000 | * |
| 1199.3591_7.285 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.356886 | 11 | 0.0380000 | * |
| 1204.4035_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.421241 | 11 | 0.0057100 | ** |
| 1204.9038_8.864 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.555463 | 11 | 0.0267000 | * |
| 1204.9948_9.897 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.437899 | 11 | 0.0329000 | * |
| 1205.4054_8.863 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.845936 | 11 | 0.0159000 | * |
| 1205.4975_9.898 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.587322 | 11 | 0.0253000 | * |
| 1216.4896_9.892 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.225108 | 11 | 0.0080900 | ** |
| 1216.9911_9.896 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.553121 | 11 | 0.0268000 | * |
| 1226.887_7.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.962983 | 11 | 0.0129000 | * |
| 1231.5117_9.905 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.757467 | 11 | 0.0186000 | * |
| 1237.8134_10.084 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.509555 | 11 | 0.0290000 | * |
| 1242.5051_10.22 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.601340 | 11 | 0.0246000 | * |
| 1242.5053_9.906 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.062344 | 11 | 0.0108000 | * |
| 1260.0333_9.997 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.490798 | 11 | 0.0300000 | * |
| 1281.8397_10.519 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.307834 | 11 | 0.0415000 | * |
| 1282.9156_6.311 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.215555 | 11 | 0.0487000 | * |
| 1285.8106_10.085 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.294039 | 11 | 0.0071500 | ** |
| 1298.1748_11.858 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.402860 | 11 | 0.0351000 | * |
| 1339.0138_6.829 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.904815 | 11 | 0.0004680 | *** |
| 1341.0272_6.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.333442 | 11 | 0.0002400 | *** |
| 1363.015_6.835 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.607274 | 11 | 0.0007560 | *** |
| 1364.2059_11.753 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.219070 | 11 | 0.0484000 | * |
| 1365.0287_6.836 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.621844 | 11 | 0.0001550 | *** |
| 1389.0311_6.813 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.627634 | 11 | 0.0001540 | *** |
| 1424.1384_8.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.549831 | 11 | 0.0270000 | * |
| 1454.1867_9.904 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.676054 | 11 | 0.0006760 | *** |
| 1454.1868_9.88 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.604527 | 11 | 0.0007590 | *** |
| 1455.1905_9.914 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.161303 | 11 | 0.0090600 | ** |
| 1473.1717_7.517 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.237906 | 11 | 0.0469000 | * |
| 1475.1518_7.004 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.334147 | 11 | 0.0066600 | ** |
| 1476.1365_7.528 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.332112 | 11 | 0.0397000 | * |
| 1479.137_7.538 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.421904 | 11 | 0.0339000 | * |
| 1481.1402_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.648344 | 11 | 0.0227000 | * |
| 1487.1502_7.525 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.410813 | 11 | 0.0346000 | * |
| 1487.1515_6.871 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.258789 | 11 | 0.0452000 | * |
| 1497.1674_7.486 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.805313 | 11 | 0.0171000 | * |
| 1499.1861_7.555 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.469085 | 11 | 0.0312000 | * |
| 1500.1349_7.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.884271 | 11 | 0.0025400 | ** |
| 1503.1775_7.514 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.888027 | 11 | 0.0148000 | * |
| 1506.089_7.132 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.686665 | 11 | 0.0212000 | * |
| 1507.0917_7.135 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.847409 | 11 | 0.0159000 | * |
| 1509.1328_6.475 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.405750 | 11 | 0.0349000 | * |
| 1510.1552_7.549 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.889388 | 11 | 0.0147000 | * |
| 1511.145_6.854 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.647010 | 11 | 0.0038400 | ** |
| 1512.1627_7.554 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.613675 | 11 | 0.0040700 | ** |
| 1514.1517_7.514 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.795103 | 11 | 0.0174000 | * |
| 1515.1602_7.514 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.232318 | 11 | 0.0473000 | * |
| 1520.1651_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.633900 | 11 | 0.0007240 | *** |
| 1523.1825_7.536 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.931365 | 11 | 0.0137000 | * |
| 1524.1333_7.404 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.426715 | 11 | 0.0336000 | * |
| 1526.0908_8.976 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.374722 | 11 | 0.0368000 | * |
| 1527.094_8.977 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.299361 | 11 | 0.0421000 | * |
| 1531.181_7.771 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.973347 | 11 | 0.0021800 | ** |
| 1531.2124_8.09 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.512239 | 11 | 0.0289000 | * |
| 1531.6527_7.711 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.109852 | 11 | 0.0017300 | ** |
| 1532.1582_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.739986 | 11 | 0.0001300 | *** |
| 1534.154_7.527 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.363101 | 11 | 0.0376000 | * |
| 1535.1521_6.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.509670 | 11 | 0.0290000 | * |
| 1535.1558_7.533 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.135544 | 11 | 0.0016600 | ** |
| 1538.1135_7.026 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.418584 | 11 | 0.0057400 | ** |
| 1538.1166_6.72 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.323005 | 11 | 0.0404000 | * |
| 1538.1514_7.472 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.845147 | 11 | 0.0159000 | * |
| 1540.1625_7.542 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.267836 | 11 | 0.0445000 | * |
| 1541.1852_7.515 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.491987 | 11 | 0.0299000 | * |
| 1542.1448_7.703 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.510543 | 11 | 0.0001830 | *** |
| 1542.2142_6.931 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.799199 | 11 | 0.0173000 | * |
| 1542.645_7.698 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.613145 | 11 | 0.0241000 | * |
| 1543.1481_7.702 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.986808 | 11 | 0.0004110 | *** |
| 1544.1549_7.686 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.614583 | 11 | 0.0241000 | * |
| 1545.1606_7.669 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.717844 | 11 | 0.0200000 | * |
| 1546.1714_7.763 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.791085 | 11 | 0.0029900 | ** |
| 1546.1749_7.78 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.668422 | 11 | 0.0037000 | ** |
| 1546.1772_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.343516 | 11 | 0.0011700 | ** |
| 1547.1793_7.789 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.017012 | 11 | 0.0117000 | * |
| 1547.1803_7.856 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.251943 | 11 | 0.0013600 | ** |
| 1547.1806_8.02 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.257466 | 11 | 0.0076300 | ** |
| 1550.1462_7.021 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.829346 | 11 | 0.0164000 | * |
| 1550.1483_7.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.718053 | 11 | 0.0200000 | * |
| 1552.1254_6.917 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.422737 | 11 | 0.0338000 | * |
| 1557.1328_6.71 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.793241 | 11 | 0.0029800 | ** |
| 1558.0818_8.319 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.016036 | 11 | 0.0117000 | * |
| 1558.1201_7.783 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.069525 | 11 | 0.0107000 | * |
| 1558.1357_6.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.115652 | 11 | 0.0017100 | ** |
| 1561.1392_7.955 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.301668 | 11 | 0.0419000 | * |
| 1562.1488_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.808247 | 11 | 0.0005460 | *** |
| 1562.1493_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.268444 | 11 | 0.0444000 | * |
| 1563.1535_7.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.880132 | 11 | 0.0001060 | *** |
| 1563.2185_8.966 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.369573 | 11 | 0.0011200 | ** |
| 1564.1662_7.508 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.151074 | 11 | 0.0016100 | ** |
| 1572.1304_7.394 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.013713 | 11 | 0.0118000 | * |
| 1574.1454_7.378 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.603838 | 11 | 0.0007600 | *** |
| 1574.2638_8.923 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.637301 | 11 | 0.0231000 | * |
| 1576.1634_7.391 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.440638 | 11 | 0.0328000 | * |
| 1577.1679_7.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.237609 | 11 | 0.0469000 | * |
| 1579.649_7.139 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.706296 | 11 | 0.0204000 | * |
| 1580.1032_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.388308 | 11 | 0.0010800 | ** |
| 1580.9848_2.737 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.466614 | 11 | 0.0313000 | * |
| 1581.1632_7.283 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.447767 | 11 | 0.0324000 | * |
| 1585.1303_6.711 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.895328 | 11 | 0.0146000 | * |
| 1585.2008_8.971 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.458777 | 11 | 0.0000126 | **** |
| 1585.6318_6.711 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.240342 | 11 | 0.0467000 | * |
| 1586.1131_6.864 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.569585 | 11 | 0.0261000 | * |
| 1586.1251_6.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.458546 | 11 | 0.0318000 | * |
| 1586.2043_8.972 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.216002 | 11 | 0.0000657 | **** |
| 1588.1988_7.697 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.419063 | 11 | 0.0341000 | * |
| 1589.2021_7.7 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.230743 | 11 | 0.0475000 | * |
| 1590.1749_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.689518 | 11 | 0.0035700 | ** |
| 1591.1826_7.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.766072 | 11 | 0.0031200 | ** |
| 1592.1507_6.918 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.438041 | 11 | 0.0329000 | * |
| 1596.3093_9.895 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.284502 | 11 | 0.0432000 | * |
| 1596.6247_6.715 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.577322 | 11 | 0.0257000 | * |
| 1597.3131_9.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.955483 | 11 | 0.0022500 | ** |
| 1598.1451_7.197 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.675640 | 11 | 0.0216000 | * |
| 1600.6232_6.854 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.733768 | 11 | 0.0194000 | * |
| 1604.1948_7.675 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.547252 | 11 | 0.0271000 | * |
| 1605.1979_7.674 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.015505 | 11 | 0.0117000 | * |
| 1606.2002_7.968 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.217449 | 11 | 0.0486000 | * |
| 1610.1103_6.827 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.431077 | 11 | 0.0333000 | * |
| 1610.32_9.892 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.324247 | 11 | 0.0403000 | * |
| 1617.1626_7.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.709793 | 11 | 0.0006400 | *** |
| 1621.1991_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.603673 | 11 | 0.0041400 | ** |
| 1621.2757_7.96 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.513383 | 11 | 0.0288000 | * |
| 1629.2009_7.725 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.864659 | 11 | 0.0026300 | ** |
| 1631.1866_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.510912 | 11 | 0.0008850 | *** |
| 1632.1881_7.956 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.464748 | 11 | 0.0052900 | ** |
| 1646.2057_7.997 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.760727 | 11 | 0.0031500 | ** |
| 1647.2105_8.001 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.627605 | 11 | 0.0039700 | ** |
| 1648.3413_9.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.795073 | 11 | 0.0174000 | * |
| 1649.3444_10.225 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.468146 | 11 | 0.0052600 | ** |
| 1649.3448_9.904 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.770484 | 11 | 0.0182000 | * |
| 205.1221_0.621 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.203038 | 11 | 0.0000175 | **** |
| 286.1437_1.13 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.552370 | 11 | 0.0008270 | *** |
| 293.0984_0.628 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.371464 | 11 | 0.0011100 | ** |
| 308.1855_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | -11.692784 | 11 | 0.0000002 | **** |
| 312.1597_1.413 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.243695 | 11 | 0.0000632 | **** |
| 315.0805_0.637 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.291372 | 11 | 0.0427000 | * |
| 331.0535_0.634 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.536381 | 11 | 0.0008490 | *** |
| 373.2483_1.147 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.348920 | 11 | 0.0011600 | ** |
| 464.1914_0.656 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.787295 | 11 | 0.0030100 | ** |
| 480.3082_3.357 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.440297 | 11 | 0.0055200 | ** |
| 494.3238_2.494 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.946906 | 11 | 0.0133000 | * |
| 502.2902_3.355 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.731487 | 11 | 0.0195000 | * |
| 508.3393_2.879 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.540528 | 11 | 0.0275000 | * |
| 519.3249_3.064 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.069860 | 11 | 0.0107000 | * |
| 522.3564_3.24 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.917152 | 11 | 0.0024000 | ** |
| 526.2644_1.714 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.892585 | 11 | 0.0000261 | **** |
| 536.437_10.971 | rel_abund_log2 | b1 | b3 | 12 | 12 | -8.537284 | 11 | 0.0000035 | **** |
| 542.3237_2.234 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.173620 | 11 | 0.0088600 | ** |
| 543.4017_4.622 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.659865 | 11 | 0.0222000 | * |
| 544.3374_3.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.479794 | 11 | 0.0051500 | ** |
| 550.3862_3.859 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.446736 | 11 | 0.0324000 | * |
| 552.4018_4.301 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.608580 | 11 | 0.0243000 | * |
| 563.4273_4.415 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.723593 | 11 | 0.0198000 | * |
| 568.4266_4.64 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.536226 | 11 | 0.0046600 | ** |
| 577.5185_8.358 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.022549 | 11 | 0.0020100 | ** |
| 578.3923_2.734 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.679200 | 11 | 0.0214000 | * |
| 593.4757_4.54 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.135627 | 11 | 0.0000736 | **** |
| 593.4762_4.644 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.083887 | 11 | 0.0000792 | **** |
| 593.4768_4.312 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.385799 | 11 | 0.0010900 | ** |
| 595.4929_4.645 | rel_abund_log2 | b1 | b3 | 12 | 12 | 8.777957 | 11 | 0.0000027 | **** |
| 596.4154_4.906 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.670589 | 11 | 0.0001440 | *** |
| 597.5086_5.242 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.478443 | 11 | 0.0000123 | **** |
| 605.5497_9.629 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.315001 | 11 | 0.0002470 | *** |
| 610.431_5.16 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.749583 | 11 | 0.0189000 | * |
| 610.5401_9.655 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.825339 | 11 | 0.0165000 | * |
| 610.5401_9.784 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.970910 | 11 | 0.0127000 | * |
| 617.4748_4.643 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.498395 | 11 | 0.0009040 | *** |
| 619.4811_4.462 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.264179 | 11 | 0.0448000 | * |
| 619.4903_4.953 | rel_abund_log2 | b1 | b3 | 12 | 12 | 7.113931 | 11 | 0.0000196 | **** |
| 623.1355_0.614 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.066781 | 11 | 0.0018600 | ** |
| 629.1522_0.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.148581 | 11 | 0.0092700 | ** |
| 634.5399_9.061 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.152931 | 11 | 0.0003170 | *** |
| 634.54_8.941 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.092662 | 11 | 0.0003480 | *** |
| 636.5558_9.845 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.622446 | 11 | 0.0237000 | * |
| 636.5558_9.995 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.452395 | 11 | 0.0321000 | * |
| 643.38_0.69 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.108471 | 11 | 0.0099500 | ** |
| 645.1222_0.627 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.888460 | 11 | 0.0004800 | *** |
| 646.6126_10.526 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.839848 | 11 | 0.0161000 | * |
| 650.6449_10.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.802311 | 11 | 0.0172000 | * |
| 690.6188_11.754 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.215897 | 11 | 0.0487000 | * |
| 691.5742_6.403 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.407718 | 11 | 0.0348000 | * |
| 704.3938_6.501 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.379313 | 11 | 0.0365000 | * |
| 717.5903_7.018 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.061996 | 11 | 0.0108000 | * |
| 718.5742_8.138 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.361426 | 11 | 0.0377000 | * |
| 719.6006_7.392 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.082736 | 11 | 0.0003530 | *** |
| 724.5277_7.806 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.447396 | 11 | 0.0324000 | * |
| 727.5745_6.067 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.687826 | 11 | 0.0211000 | * |
| 729.5912_6.714 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.482661 | 11 | 0.0051200 | ** |
| 732.5542_6.723 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.709267 | 11 | 0.0203000 | * |
| 732.589_8.53 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.219182 | 11 | 0.0484000 | * |
| 738.5064_6.603 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.495743 | 11 | 0.0297000 | * |
| 738.543_8.181 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.252257 | 11 | 0.0013600 | ** |
| 740.5559_8.156 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.840597 | 11 | 0.0161000 | * |
| 742.5753_7.523 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.705037 | 11 | 0.0205000 | * |
| 743.6054_7.227 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.919879 | 11 | 0.0001000 | **** |
| 745.6202_7.852 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.131762 | 11 | 0.0095500 | ** |
| 745.6214_8.115 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.429825 | 11 | 0.0334000 | * |
| 746.5091_7.814 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.925768 | 11 | 0.0138000 | * |
| 748.5258_8.233 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.217273 | 11 | 0.0486000 | * |
| 749.5557_6.066 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.086577 | 11 | 0.0103000 | * |
| 751.5717_6.812 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.274171 | 11 | 0.0440000 | * |
| 752.5592_8.969 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.222425 | 11 | 0.0482000 | * |
| 752.5594_8.973 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.220889 | 11 | 0.0483000 | * |
| 755.6054_6.838 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.504610 | 11 | 0.0049300 | ** |
| 759.4494_6.981 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.841271 | 11 | 0.0160000 | * |
| 760.421_6.983 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.642327 | 11 | 0.0229000 | * |
| 760.4925_6.98 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.322611 | 11 | 0.0404000 | * |
| 760.5281_6.984 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.151766 | 11 | 0.0092100 | ** |
| 760.5406_6.984 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.946902 | 11 | 0.0022800 | ** |
| 760.5459_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.796156 | 11 | 0.0174000 | * |
| 760.5883_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.299590 | 11 | 0.0000584 | **** |
| 760.9763_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.532211 | 11 | 0.0047000 | ** |
| 761.1927_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.191564 | 11 | 0.0085800 | ** |
| 761.2595_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.658534 | 11 | 0.0037600 | ** |
| 761.302_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.097205 | 11 | 0.0003460 | *** |
| 761.3174_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.686113 | 11 | 0.0212000 | * |
| 761.329_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.269784 | 11 | 0.0074700 | ** |
| 761.3681_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.318854 | 11 | 0.0012200 | ** |
| 761.3781_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.707556 | 11 | 0.0034600 | ** |
| 761.4062_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.224083 | 11 | 0.0081000 | ** |
| 761.4141_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.273631 | 11 | 0.0013100 | ** |
| 761.4437_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.590018 | 11 | 0.0042400 | ** |
| 761.4545_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.354470 | 11 | 0.0011500 | ** |
| 761.5191_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.686045 | 11 | 0.0006650 | *** |
| 762.5032_7.719 | rel_abund_log2 | b1 | b3 | 12 | 12 | 5.043618 | 11 | 0.0003760 | *** |
| 762.5251_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.202730 | 11 | 0.0084100 | ** |
| 762.5433_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.514086 | 11 | 0.0048500 | ** |
| 764.5195_7.458 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.206216 | 11 | 0.0495000 | * |
| 766.5713_8.323 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.863274 | 11 | 0.0026400 | ** |
| 766.5755_7.417 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.833479 | 11 | 0.0005240 | *** |
| 768.551_7.206 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.182754 | 11 | 0.0087200 | ** |
| 768.5536_6.434 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.415815 | 11 | 0.0343000 | * |
| 768.5859_8.446 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.812600 | 11 | 0.0169000 | * |
| 768.587_8.451 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.670088 | 11 | 0.0218000 | * |
| 768.5909_7.579 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.539628 | 11 | 0.0275000 | * |
| 771.5762_7.712 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.427648 | 11 | 0.0010200 | ** |
| 773.6516_9.025 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.923204 | 11 | 0.0023800 | ** |
| 780.5549_6.325 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.265280 | 11 | 0.0447000 | * |
| 780.5921_7.79 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.047844 | 11 | 0.0019200 | ** |
| 781.6192_8.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.315082 | 11 | 0.0068900 | ** |
| 781.62_7.043 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.517666 | 11 | 0.0001810 | *** |
| 783.2313_6.877 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.495940 | 11 | 0.0297000 | * |
| 783.6368_7.847 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.184435 | 11 | 0.0000687 | **** |
| 784.5879_7.141 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.216599 | 11 | 0.0487000 | * |
| 784.6654_10.38 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.642904 | 11 | 0.0229000 | * |
| 784.779_6.877 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.422941 | 11 | 0.0338000 | * |
| 785.0064_6.878 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.620589 | 11 | 0.0238000 | * |
| 785.0201_6.879 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.603001 | 11 | 0.0246000 | * |
| 785.0389_6.88 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.676730 | 11 | 0.0215000 | * |
| 785.6536_8.747 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.897012 | 11 | 0.0145000 | * |
| 786.5814_6.876 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.294990 | 11 | 0.0071400 | ** |
| 787.6674_9.202 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.800036 | 11 | 0.0005530 | *** |
| 787.6701_9.896 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.369448 | 11 | 0.0372000 | * |
| 788.5563_7.415 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.413750 | 11 | 0.0000498 | **** |
| 788.6177_8.867 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.487333 | 11 | 0.0302000 | * |
| 790.5744_7.193 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.924007 | 11 | 0.0023800 | ** |
| 791.5433_6.743 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.325197 | 11 | 0.0067700 | ** |
| 792.5531_6.261 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.644635 | 11 | 0.0228000 | * |
| 792.5534_8.102 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.337666 | 11 | 0.0393000 | * |
| 792.5893_7.058 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.020604 | 11 | 0.0116000 | * |
| 792.5899_7.556 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.958261 | 11 | 0.0130000 | * |
| 794.6054_8.214 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.720625 | 11 | 0.0033800 | ** |
| 794.6057_8.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.235984 | 11 | 0.0470000 | * |
| 794.6062_7.691 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.433692 | 11 | 0.0332000 | * |
| 796.5845_7.27 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.742574 | 11 | 0.0191000 | * |
| 796.5848_7.423 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.620937 | 11 | 0.0007390 | *** |
| 796.621_8.383 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.456822 | 11 | 0.0319000 | * |
| 797.6383_8.603 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.389415 | 11 | 0.0060400 | ** |
| 797.6453_8.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.778933 | 11 | 0.0005720 | *** |
| 798.5402_7.73 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.734966 | 11 | 0.0194000 | * |
| 798.6003_7.83 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.531295 | 11 | 0.0279000 | * |
| 798.6518_8.391 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.954199 | 11 | 0.0000953 | **** |
| 799.6069_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.294520 | 11 | 0.0424000 | * |
| 799.6686_9.32 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.160291 | 11 | 0.0015900 | ** |
| 802.5357_6.323 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.458205 | 11 | 0.0318000 | * |
| 802.5717_7.81 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.075678 | 11 | 0.0106000 | * |
| 804.5937_7.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.568849 | 11 | 0.0261000 | * |
| 805.0108_3.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.622544 | 11 | 0.0040100 | ** |
| 805.6187_7.848 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.117799 | 11 | 0.0017100 | ** |
| 805.6784_9.291 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.919403 | 11 | 0.0140000 | * |
| 807.6349_8.948 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.663819 | 11 | 0.0006890 | *** |
| 807.6359_7.483 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.110564 | 11 | 0.0003380 | *** |
| 808.6375_8.751 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.287272 | 11 | 0.0072400 | ** |
| 809.6507_9.876 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.399918 | 11 | 0.0059300 | ** |
| 809.6525_8.065 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.032616 | 11 | 0.0019700 | ** |
| 810.5988_8.865 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.584515 | 11 | 0.0254000 | * |
| 810.6013_7.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.294571 | 11 | 0.0424000 | * |
| 810.6041_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.003128 | 11 | 0.0120000 | * |
| 810.6564_8.03 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.611987 | 11 | 0.0040800 | ** |
| 810.6812_10.387 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.071393 | 11 | 0.0106000 | * |
| 811.0052_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.611903 | 11 | 0.0242000 | * |
| 811.2977_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.473330 | 11 | 0.0309000 | * |
| 811.4149_7.961 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.720729 | 11 | 0.0199000 | * |
| 811.4537_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.595710 | 11 | 0.0249000 | * |
| 811.5374_7.96 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.338350 | 11 | 0.0393000 | * |
| 811.6292_7.819 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.317591 | 11 | 0.0068600 | ** |
| 811.6703_8.961 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.477834 | 11 | 0.0051700 | ** |
| 812.5561_7.198 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.677711 | 11 | 0.0036400 | ** |
| 813.6106_7.959 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.453038 | 11 | 0.0321000 | * |
| 813.6834_9.437 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.615418 | 11 | 0.0007460 | *** |
| 813.6866_9.902 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.406519 | 11 | 0.0348000 | * |
| 814.5355_8.122 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.224469 | 11 | 0.0080900 | ** |
| 814.535_6.26 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.251281 | 11 | 0.0458000 | * |
| 814.6127_7.96 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.496902 | 11 | 0.0297000 | * |
| 815.7025_10.568 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.795121 | 11 | 0.0174000 | * |
| 816.5872_8.538 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.587620 | 11 | 0.0252000 | * |
| 817.7051_10.344 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.133261 | 11 | 0.0095200 | ** |
| 818.5668_7.42 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.005333 | 11 | 0.0120000 | * |
| 818.6049_7.793 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.303713 | 11 | 0.0070300 | ** |
| 819.6577_6.728 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.282880 | 11 | 0.0073000 | ** |
| 820.5836_6.837 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.769597 | 11 | 0.0182000 | * |
| 820.5846_7.213 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.709193 | 11 | 0.0034500 | ** |
| 820.6202_7.907 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.565304 | 11 | 0.0000405 | **** |
| 820.6961_11.574 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.272267 | 11 | 0.0000607 | **** |
| 822.5994_7.509 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.140725 | 11 | 0.0003230 | *** |
| 824.616_8.544 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.352586 | 11 | 0.0383000 | * |
| 824.6209_7.408 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.452299 | 11 | 0.0054100 | ** |
| 824.6527_9.167 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.284485 | 11 | 0.0432000 | * |
| 825.6241_9.886 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.513064 | 11 | 0.0288000 | * |
| 825.6473_7.81 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.659947 | 11 | 0.0222000 | * |
| 825.6778_9.575 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.265566 | 11 | 0.0013300 | ** |
| 825.6815_9.335 | rel_abund_log2 | b1 | b3 | 12 | 12 | -9.279000 | 11 | 0.0000016 | **** |
| 825.682_9.561 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.589082 | 11 | 0.0042500 | ** |
| 826.5716_8.868 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.366507 | 11 | 0.0374000 | * |
| 827.663_8.483 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.978174 | 11 | 0.0004170 | *** |
| 827.6998_10.277 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.660916 | 11 | 0.0000356 | **** |
| 827.6999_10.389 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.659058 | 11 | 0.0001470 | *** |
| 827.6999_10.502 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.520035 | 11 | 0.0285000 | * |
| 828.5522_5.728 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.402752 | 11 | 0.0351000 | * |
| 828.6106_7.766 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.334177 | 11 | 0.0396000 | * |
| 829.6782_8.401 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.322869 | 11 | 0.0012100 | ** |
| 830.6471_9.551 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.536399 | 11 | 0.0046600 | ** |
| 831.57_7.208 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.348517 | 11 | 0.0386000 | * |
| 831.6353_8.196 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.429552 | 11 | 0.0002070 | *** |
| 832.582_7.631 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.581294 | 11 | 0.0043100 | ** |
| 834.6002_7.257 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.639789 | 11 | 0.0007170 | *** |
| 834.602_7.751 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.958872 | 11 | 0.0130000 | * |
| 835.6663_8.407 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.459352 | 11 | 0.0053400 | ** |
| 835.6664_10.22 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.495114 | 11 | 0.0298000 | * |
| 835.6666_9.91 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.947588 | 11 | 0.0022800 | ** |
| 836.6148_8.439 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.202123 | 11 | 0.0499000 | * |
| 837.5477_8.363 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.567409 | 11 | 0.0008070 | *** |
| 837.6724_9.906 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.533839 | 11 | 0.0008520 | *** |
| 839.6989_10.178 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.815759 | 11 | 0.0168000 | * |
| 842.638_7.268 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.546429 | 11 | 0.0272000 | * |
| 842.723_11.155 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.294663 | 11 | 0.0424000 | * |
| 844.7397_11.265 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.409950 | 11 | 0.0346000 | * |
| 846.6641_8.878 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.287845 | 11 | 0.0429000 | * |
| 847.6893_7.716 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.521152 | 11 | 0.0047900 | ** |
| 848.6521_9.028 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.509323 | 11 | 0.0001840 | *** |
| 849.6682_6.746 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.894409 | 11 | 0.0146000 | * |
| 854.4963_2.738 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.448216 | 11 | 0.0323000 | * |
| 854.574_8.362 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.157517 | 11 | 0.0000713 | **** |
| 855.6216_6.322 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.427139 | 11 | 0.0336000 | * |
| 856.5819_7.24 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.707520 | 11 | 0.0001370 | *** |
| 856.5823_7.778 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.750779 | 11 | 0.0032100 | ** |
| 858.5914_7.792 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.237207 | 11 | 0.0079100 | ** |
| 858.5977_8.453 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.993840 | 11 | 0.0122000 | * |
| 858.5992_7.06 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.983599 | 11 | 0.0021500 | ** |
| 858.7545_11.345 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.056433 | 11 | 0.0109000 | * |
| 859.53_8.35 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.511522 | 11 | 0.0008840 | *** |
| 859.6891_7.519 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.081250 | 11 | 0.0104000 | * |
| 860.6133_8.785 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.609245 | 11 | 0.0243000 | * |
| 861.7048_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.925017 | 11 | 0.0023700 | ** |
| 863.6838_8.874 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.856332 | 11 | 0.0156000 | * |
| 863.6839_6.733 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.684970 | 11 | 0.0212000 | * |
| 863.7092_11.343 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.920883 | 11 | 0.0139000 | * |
| 865.5795_9.626 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.358050 | 11 | 0.0011400 | ** |
| 868.7389_11.19 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.911197 | 11 | 0.0142000 | * |
| 870.6336_9.039 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.403492 | 11 | 0.0000506 | **** |
| 870.7558_11.296 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.128270 | 11 | 0.0016800 | ** |
| 871.6776_11.111 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.208492 | 11 | 0.0493000 | * |
| 872.5379_6.428 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.370879 | 11 | 0.0371000 | * |
| 872.6526_8.477 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.693492 | 11 | 0.0006570 | *** |
| 872.652_8.777 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.936031 | 11 | 0.0023300 | ** |
| 872.7731_11.418 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.053025 | 11 | 0.0019100 | ** |
| 873.7045_7.52 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.451144 | 11 | 0.0322000 | * |
| 874.6655_10.042 | rel_abund_log2 | b1 | b3 | 12 | 12 | -7.151698 | 11 | 0.0000186 | **** |
| 874.7461_11.559 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.257252 | 11 | 0.0453000 | * |
| 875.7095_11.292 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.562920 | 11 | 0.0044500 | ** |
| 875.7207_8.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.365050 | 11 | 0.0375000 | * |
| 876.5695_7.88 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.286973 | 11 | 0.0430000 | * |
| 876.6835_10.245 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.880155 | 11 | 0.0000266 | **** |
| 876.6836_10.537 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.315502 | 11 | 0.0409000 | * |
| 876.854_11.56 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.619942 | 11 | 0.0238000 | * |
| 877.6374_9.86 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.866733 | 11 | 0.0026200 | ** |
| 877.6374_9.988 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.620523 | 11 | 0.0238000 | * |
| 877.7_7.713 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.564642 | 11 | 0.0263000 | * |
| 878.5852_8.857 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.279151 | 11 | 0.0436000 | * |
| 878.7033_7.717 | rel_abund_log2 | b1 | b3 | 12 | 12 | 6.785827 | 11 | 0.0000301 | **** |
| 879.7408_11.578 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.505650 | 11 | 0.0292000 | * |
| 880.718_11.248 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.511851 | 11 | 0.0289000 | * |
| 883.6892_7.419 | rel_abund_log2 | b1 | b3 | 12 | 12 | -5.423001 | 11 | 0.0002090 | *** |
| 883.6894_6.894 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.453436 | 11 | 0.0320000 | * |
| 884.7697_11.358 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.945413 | 11 | 0.0022900 | ** |
| 885.7047_7.587 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.770862 | 11 | 0.0182000 | * |
| 886.7858_11.492 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.577744 | 11 | 0.0043400 | ** |
| 891.7156_7.718 | rel_abund_log2 | b1 | b3 | 12 | 12 | 4.087962 | 11 | 0.0018000 | ** |
| 891.7406_11.489 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.302745 | 11 | 0.0070400 | ** |
| 892.7383_11.126 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.819218 | 11 | 0.0167000 | * |
| 894.7548_11.21 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.405735 | 11 | 0.0058700 | ** |
| 896.7714_11.316 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.720716 | 11 | 0.0033800 | ** |
| 897.7047_7.419 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.718995 | 11 | 0.0006300 | *** |
| 897.705_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.091411 | 11 | 0.0103000 | * |
| 898.7872_11.44 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.438121 | 11 | 0.0329000 | * |
| 899.7091_11.203 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.549683 | 11 | 0.0008310 | *** |
| 900.6836_9.96 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.963738 | 11 | 0.0004260 | *** |
| 901.6999_7.143 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.762940 | 11 | 0.0031400 | ** |
| 901.7251_11.318 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.665574 | 11 | 0.0220000 | * |
| 907.6886_7.2 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.370170 | 11 | 0.0371000 | * |
| 911.5614_9.119 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.417794 | 11 | 0.0057500 | ** |
| 911.7203_7.674 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.700105 | 11 | 0.0207000 | * |
| 911.7206_7.958 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.418093 | 11 | 0.0341000 | * |
| 912.8727_11.58 | rel_abund_log2 | b1 | b3 | 12 | 12 | 3.012375 | 11 | 0.0118000 | * |
| 913.7001_6.872 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.430193 | 11 | 0.0334000 | * |
| 918.754_11.178 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.429060 | 11 | 0.0335000 | * |
| 919.7465_8.87 | rel_abund_log2 | b1 | b3 | 12 | 12 | 2.830942 | 11 | 0.0163000 | * |
| 922.7862_11.386 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.172938 | 11 | 0.0088700 | ** |
| 924.8016_11.511 | rel_abund_log2 | b1 | b3 | 12 | 12 | -6.292248 | 11 | 0.0000590 | **** |
| 926.8159_11.647 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.973241 | 11 | 0.0127000 | * |
| 927.7399_11.374 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.567552 | 11 | 0.0262000 | * |
| 928.7831_8.964 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.391864 | 11 | 0.0060200 | ** |
| 931.7715_11.646 | rel_abund_log2 | b1 | b3 | 12 | 12 | -4.320578 | 11 | 0.0012100 | ** |
| 933.7875_11.787 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.311993 | 11 | 0.0412000 | * |
| 936.8015_11.483 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.615848 | 11 | 0.0240000 | * |
| 937.6995_6.371 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.953401 | 11 | 0.0131000 | * |
| 938.8159_11.193 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.231213 | 11 | 0.0080000 | ** |
| 941.7308_7.633 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.592930 | 11 | 0.0250000 | * |
| 941.7313_7.957 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.824960 | 11 | 0.0165000 | * |
| 942.7986_8.961 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.279060 | 11 | 0.0436000 | * |
| 950.8156_11.512 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.010495 | 11 | 0.0119000 | * |
| 951.7153_7.77 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.600807 | 11 | 0.0247000 | * |
| 952.831_11.66 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.110511 | 11 | 0.0099200 | ** |
| 957.7865_11.655 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.166048 | 11 | 0.0089800 | ** |
| 972.8_11.345 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.247276 | 11 | 0.0461000 | * |
| 978.8456_11.66 | rel_abund_log2 | b1 | b3 | 12 | 12 | -3.276276 | 11 | 0.0073800 | ** |
| 993.7984_10.238 | rel_abund_log2 | b1 | b3 | 12 | 12 | -2.862384 | 11 | 0.0155000 | * |
## [1] 452
# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_redANDctrl <- full_join(red_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".red", ".ctrl"))
# number of features in combined list
nrow(fulljoin_t.test_redANDctrl)## [1] 632
Let’s try and remove features significant due to the background diet
sig_paired_red_rmBG <- fulljoin_t.test_redANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.red * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features without bg diet effect
nrow(sig_paired_red_rmBG)## [1] 195
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 257
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
red_sig_ANOVA_overlap_paired <- inner_join(sig_paired_red_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(red_sig_ANOVA_overlap_paired$mz_rt)## [1] "1526.0908_8.976" "1527.094_8.977" "1649.3448_9.904" "536.437_10.971"
## [5] "605.5497_9.629" "738.543_8.181" "748.5258_8.233" "785.0201_6.879"
## [9] "797.6453_8.401" "813.6866_9.902" "827.6999_10.389" "865.5795_9.626"
## [13] "876.5695_7.88" "878.5852_8.857" "897.705_7.958"
# run paired t-tests for control intervention
tomato_t.test_paired <- df_for_stats %>%
filter(tomato_or_control == "tomato") %>%
dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ period,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
tomato_t.test_paired_sig <- tomato_t.test_paired %>%
filter(p < 0.05)
kable(tomato_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1014.9413_11.924 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.756628 | 23 | 0.0112000 | * |
| 1017.6873_3.095 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.816592 | 23 | 0.0008860 | *** |
| 1018.1281_3.082 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.446994 | 23 | 0.0225000 | * |
| 1020.8931_11.508 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.464274 | 23 | 0.0216000 | * |
| 1026.6606_3.098 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.352023 | 23 | 0.0276000 | * |
| 1028.9572_12.137 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.672502 | 23 | 0.0136000 | * |
| 1034.9093_11.511 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.411410 | 23 | 0.0023900 | ** |
| 1036.925_11.654 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.556487 | 23 | 0.0016800 | ** |
| 1039.6688_3.106 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.199101 | 23 | 0.0003430 | *** |
| 1039.6724_2.735 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.186184 | 23 | 0.0392000 | * |
| 1041.9353_11.754 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.638464 | 23 | 0.0001150 | *** |
| 1061.6537_2.738 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.110463 | 23 | 0.0459000 | * |
| 1061.8543_10.506 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.652203 | 23 | 0.0013300 | ** |
| 1128.671_2.722 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.024166 | 23 | 0.0060400 | ** |
| 1151.3639_7.711 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.065182 | 23 | 0.0004780 | *** |
| 1151.8656_7.71 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.988312 | 23 | 0.0000480 | **** |
| 1155.8333_6.446 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.446795 | 23 | 0.0225000 | * |
| 1157.3434_6.852 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.435051 | 23 | 0.0231000 | * |
| 116.0707_0.623 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.507298 | 23 | 0.0018900 | ** |
| 1163.3585_7.687 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.350224 | 23 | 0.0027700 | ** |
| 1169.3422_6.843 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.396459 | 23 | 0.0251000 | * |
| 1171.3299_6.847 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.072837 | 23 | 0.0496000 | * |
| 118.0864_0.597 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.032818 | 23 | 0.0000004 | **** |
| 1187.3609_7.137 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.288973 | 23 | 0.0032100 | ** |
| 1187.8641_7.656 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.141304 | 23 | 0.0431000 | * |
| 1187.8642_7.137 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.435475 | 23 | 0.0230000 | * |
| 1187.8649_7.285 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.362362 | 23 | 0.0270000 | * |
| 1189.3305_6.84 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.366287 | 23 | 0.0268000 | * |
| 1192.8412_6.709 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.482705 | 23 | 0.0020100 | ** |
| 1193.9116_8.867 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.979780 | 23 | 0.0005920 | *** |
| 1195.8336_6.857 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.612341 | 23 | 0.0156000 | * |
| 1198.856_7.141 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.658633 | 23 | 0.0140000 | * |
| 1199.3591_7.285 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.454670 | 23 | 0.0221000 | * |
| 1199.864_7.757 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.126008 | 23 | 0.0445000 | * |
| 1204.4035_8.865 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.845184 | 23 | 0.0008260 | *** |
| 1204.9038_8.864 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.664984 | 23 | 0.0012900 | ** |
| 1204.9948_9.897 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.904721 | 23 | 0.0007130 | *** |
| 1205.4054_8.863 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.173292 | 23 | 0.0003660 | *** |
| 1205.4975_9.898 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.811819 | 23 | 0.0008970 | *** |
| 1210.8558_7.304 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.598275 | 23 | 0.0161000 | * |
| 1216.4896_9.892 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.497473 | 23 | 0.0001630 | *** |
| 1216.9911_9.896 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.691952 | 23 | 0.0001000 | **** |
| 1225.3783_7.964 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.876339 | 23 | 0.0085300 | ** |
| 1225.88_7.956 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.146726 | 23 | 0.0426000 | * |
| 1226.887_7.956 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.907790 | 23 | 0.0079300 | ** |
| 1229.9108_8.397 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.070067 | 23 | 0.0054200 | ** |
| 1229.9993_9.9 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.893940 | 23 | 0.0081900 | ** |
| 1231.5117_9.905 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.284925 | 23 | 0.0002770 | *** |
| 1237.8134_10.084 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.074231 | 23 | 0.0053700 | ** |
| 1241.4043_8.395 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.853972 | 23 | 0.0089800 | ** |
| 1242.5051_10.22 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.673215 | 23 | 0.0012600 | ** |
| 1242.5053_9.906 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.582366 | 23 | 0.0001320 | *** |
| 1249.7383_5.838 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.157385 | 23 | 0.0417000 | * |
| 1259.7951_10.087 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.835315 | 23 | 0.0093800 | ** |
| 1263.8289_10.086 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.384164 | 23 | 0.0258000 | * |
| 1263.8289_10.349 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.680038 | 23 | 0.0012400 | ** |
| 1265.8449_10.582 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.276398 | 23 | 0.0324000 | * |
| 1268.2053_12.079 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.249827 | 23 | 0.0343000 | * |
| 1276.1913_12.079 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.103715 | 23 | 0.0465000 | * |
| 1281.8397_10.519 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.692553 | 23 | 0.0012000 | ** |
| 1285.8106_10.085 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.479606 | 23 | 0.0020300 | ** |
| 1297.1947_10.828 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.701741 | 23 | 0.0127000 | * |
| 1298.1748_11.858 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.223626 | 23 | 0.0363000 | * |
| 1298.1983_10.827 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.290609 | 23 | 0.0032000 | ** |
| 1299.1762_12.078 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.075740 | 23 | 0.0493000 | * |
| 1302.2067_12.075 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.278947 | 23 | 0.0323000 | * |
| 1308.157_11.948 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.685996 | 23 | 0.0132000 | * |
| 1316.1245_11.664 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.812332 | 23 | 0.0098900 | ** |
| 1318.2126_11.857 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.121279 | 23 | 0.0449000 | * |
| 1319.2346_12.075 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.568846 | 23 | 0.0172000 | * |
| 1322.2624_10.832 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.539935 | 23 | 0.0017500 | ** |
| 1334.1724_11.947 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.840458 | 23 | 0.0092700 | ** |
| 1339.0138_6.829 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.712884 | 23 | 0.0000952 | **** |
| 1339.2017_11.786 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.353661 | 23 | 0.0275000 | * |
| 1341.0272_6.83 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.709964 | 23 | 0.0000082 | **** |
| 1341.2177_11.895 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.988043 | 23 | 0.0005790 | *** |
| 1342.2211_11.896 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.070919 | 23 | 0.0054100 | ** |
| 1346.1728_11.908 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.523353 | 23 | 0.0018200 | ** |
| 1347.1765_11.909 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.074887 | 23 | 0.0053600 | ** |
| 1363.015_6.835 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.190933 | 23 | 0.0003500 | *** |
| 1364.2059_11.753 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.184076 | 23 | 0.0003560 | *** |
| 1365.0287_6.836 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.106371 | 23 | 0.0000003 | **** |
| 1369.1611_11.766 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.460842 | 23 | 0.0218000 | * |
| 1389.0311_6.813 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.381553 | 23 | 0.0000182 | **** |
| 1424.1384_8.941 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.070168 | 23 | 0.0498000 | * |
| 1447.1208_6.507 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.463114 | 23 | 0.0217000 | * |
| 1454.1867_9.904 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.025228 | 23 | 0.0000438 | **** |
| 1454.1868_9.88 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.067277 | 23 | 0.0000395 | **** |
| 1455.1905_9.914 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.145057 | 23 | 0.0003920 | *** |
| 1465.1678_7.525 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.140800 | 23 | 0.0431000 | * |
| 1470.0283_7.811 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.435923 | 23 | 0.0022500 | ** |
| 1472.1218_6.472 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.895900 | 23 | 0.0081500 | ** |
| 1473.1717_7.517 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.883038 | 23 | 0.0007520 | *** |
| 1475.1518_7.004 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.399438 | 23 | 0.0000174 | **** |
| 1476.1365_7.528 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.410957 | 23 | 0.0002020 | *** |
| 1476.1552_7.005 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.278709 | 23 | 0.0002810 | *** |
| 1478.1161_7.023 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.608798 | 23 | 0.0157000 | * |
| 1479.1185_7.024 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.398538 | 23 | 0.0250000 | * |
| 1479.137_7.538 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.411540 | 23 | 0.0023900 | ** |
| 1481.1402_7.792 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.049810 | 23 | 0.0004970 | *** |
| 1487.1502_7.525 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.864276 | 23 | 0.0087700 | ** |
| 1487.1511_6.872 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.613269 | 23 | 0.0155000 | * |
| 1487.1515_6.871 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.676855 | 23 | 0.0135000 | * |
| 1488.1552_6.873 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.550125 | 23 | 0.0017100 | ** |
| 1497.1674_7.486 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.851900 | 23 | 0.0000674 | **** |
| 1499.1861_7.555 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.738345 | 23 | 0.0117000 | * |
| 1500.0993_6.518 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.402836 | 23 | 0.0247000 | * |
| 1500.1349_7.508 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.059333 | 23 | 0.0000035 | **** |
| 1502.1505_7.558 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.294506 | 23 | 0.0312000 | * |
| 1503.1775_7.514 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.555069 | 23 | 0.0016900 | ** |
| 1504.1822_7.513 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.566863 | 23 | 0.0172000 | * |
| 1506.089_7.132 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.706950 | 23 | 0.0126000 | * |
| 1506.1473_7.519 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.937593 | 23 | 0.0074000 | ** |
| 1507.0917_7.135 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.245667 | 23 | 0.0346000 | * |
| 1507.1178_6.465 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.127343 | 23 | 0.0443000 | * |
| 1508.1468_7.513 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.280851 | 23 | 0.0321000 | * |
| 1508.1474_7.516 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.536291 | 23 | 0.0017600 | ** |
| 1509.1328_6.475 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.973257 | 23 | 0.0068000 | ** |
| 1509.1474_7.507 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.106856 | 23 | 0.0004310 | *** |
| 1510.1192_7.742 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.091928 | 23 | 0.0477000 | * |
| 1510.1192_7.922 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.198173 | 23 | 0.0383000 | * |
| 1510.1351_6.854 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.422800 | 23 | 0.0237000 | * |
| 1510.1375_6.48 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.602230 | 23 | 0.0159000 | * |
| 1510.1552_7.549 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.209539 | 23 | 0.0003340 | *** |
| 1511.123_7.922 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.277119 | 23 | 0.0033100 | ** |
| 1511.145_6.854 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.576163 | 23 | 0.0016000 | ** |
| 1512.1627_7.554 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.194264 | 23 | 0.0040300 | ** |
| 1514.1517_7.514 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.487575 | 23 | 0.0001670 | *** |
| 1515.1602_7.514 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.480254 | 23 | 0.0209000 | * |
| 1518.2654_8.731 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.360977 | 23 | 0.0027000 | ** |
| 1520.1651_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.422223 | 23 | 0.0000165 | **** |
| 1523.1825_7.536 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.710240 | 23 | 0.0125000 | * |
| 1524.1333_7.404 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.821564 | 23 | 0.0008750 | *** |
| 1526.0908_8.976 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.080666 | 23 | 0.0004600 | *** |
| 1526.1136_6.481 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.540820 | 23 | 0.0183000 | * |
| 1526.1519_7.532 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.772792 | 23 | 0.0108000 | * |
| 1527.094_8.977 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.184747 | 23 | 0.0003550 | *** |
| 1527.1556_7.532 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.552367 | 23 | 0.0178000 | * |
| 1530.1768_7.745 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.278844 | 23 | 0.0323000 | * |
| 1531.1683_7.726 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.165262 | 23 | 0.0410000 | * |
| 1531.181_7.771 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.986523 | 23 | 0.0000482 | **** |
| 1531.2124_8.09 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.796455 | 23 | 0.0000773 | **** |
| 1531.6527_7.711 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.751606 | 23 | 0.0010400 | ** |
| 1532.1012_7.882 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.416722 | 23 | 0.0240000 | * |
| 1532.1582_7.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.644479 | 23 | 0.0000096 | **** |
| 1534.154_7.527 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.409517 | 23 | 0.0002030 | *** |
| 1534.1768_8.083 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.730872 | 23 | 0.0119000 | * |
| 1535.1521_6.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.704900 | 23 | 0.0126000 | * |
| 1535.1558_7.533 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.421341 | 23 | 0.0001970 | *** |
| 1536.6252_6.856 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.973773 | 23 | 0.0068000 | ** |
| 1537.1733_7.573 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.831065 | 23 | 0.0094700 | ** |
| 1538.1135_7.026 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.609268 | 23 | 0.0157000 | * |
| 1538.1514_7.472 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.926246 | 23 | 0.0000560 | **** |
| 1540.1625_7.542 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.046579 | 23 | 0.0057300 | ** |
| 1540.1703_7.761 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.709767 | 23 | 0.0011500 | ** |
| 1541.1773_7.535 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.532959 | 23 | 0.0017800 | ** |
| 1541.1852_7.515 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.334909 | 23 | 0.0028800 | ** |
| 1542.1448_7.703 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.085352 | 23 | 0.0004550 | *** |
| 1542.1805_8.073 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.170637 | 23 | 0.0405000 | * |
| 1542.2142_6.931 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.557678 | 23 | 0.0176000 | * |
| 1542.645_7.698 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.478004 | 23 | 0.0020300 | ** |
| 1543.1481_7.702 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.093289 | 23 | 0.0004460 | *** |
| 1543.2104_7.94 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.058835 | 23 | 0.0004860 | *** |
| 1544.1542_7.691 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.533647 | 23 | 0.0186000 | * |
| 1544.1549_7.686 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.211538 | 23 | 0.0038700 | ** |
| 1544.1615_7.508 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.584622 | 23 | 0.0166000 | * |
| 1545.1606_7.669 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.228903 | 23 | 0.0359000 | * |
| 1546.1714_7.763 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.632757 | 23 | 0.0001160 | *** |
| 1546.1749_7.78 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.877684 | 23 | 0.0000632 | **** |
| 1546.1772_8.02 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.140138 | 23 | 0.0045900 | ** |
| 1547.1793_7.789 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.959732 | 23 | 0.0006220 | *** |
| 1547.1803_7.856 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.958637 | 23 | 0.0000517 | **** |
| 1547.1806_8.02 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.952200 | 23 | 0.0006330 | *** |
| 1548.0963_7.222 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.391455 | 23 | 0.0254000 | * |
| 1550.1138_6.449 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.521698 | 23 | 0.0018300 | ** |
| 1550.1462_7.021 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.059126 | 23 | 0.0055600 | ** |
| 1550.1483_7.401 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.997849 | 23 | 0.0000469 | **** |
| 1554.1833_7.701 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.829459 | 23 | 0.0095000 | ** |
| 1557.1328_6.71 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.139848 | 23 | 0.0045900 | ** |
| 1558.0818_8.319 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.471428 | 23 | 0.0213000 | * |
| 1558.1201_7.783 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.103167 | 23 | 0.0004350 | *** |
| 1558.1357_6.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.797247 | 23 | 0.0009300 | *** |
| 1560.2277_8.857 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.367304 | 23 | 0.0267000 | * |
| 1561.1392_7.955 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.441087 | 23 | 0.0022300 | ** |
| 1561.2317_8.857 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.615576 | 23 | 0.0155000 | * |
| 1562.1488_7.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.882982 | 23 | 0.0000005 | **** |
| 1562.1493_7.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.647452 | 23 | 0.0001120 | *** |
| 1563.1166_6.865 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.570533 | 23 | 0.0171000 | * |
| 1563.1535_7.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -8.727794 | 23 | 0.0000000 | **** |
| 1563.2185_8.966 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.915555 | 23 | 0.0000050 | **** |
| 1564.1347_6.888 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.034352 | 23 | 0.0058900 | ** |
| 1564.1662_7.508 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.217058 | 23 | 0.0000273 | **** |
| 1565.1315_7.095 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.163874 | 23 | 0.0411000 | * |
| 1566.1816_7.932 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.863905 | 23 | 0.0007880 | *** |
| 1566.2163_6.888 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.853777 | 23 | 0.0089800 | ** |
| 1566.6417_7.129 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.273569 | 23 | 0.0326000 | * |
| 1567.1909_7.931 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.230999 | 23 | 0.0036900 | ** |
| 1569.2427_7.289 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.119620 | 23 | 0.0450000 | * |
| 1572.1304_7.394 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.706589 | 23 | 0.0000967 | **** |
| 1574.1454_7.378 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.847714 | 23 | 0.0000681 | **** |
| 1574.2638_8.923 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.074105 | 23 | 0.0000388 | **** |
| 1574.6139_6.862 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.147372 | 23 | 0.0425000 | * |
| 1576.1618_7.678 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.162281 | 23 | 0.0412000 | * |
| 1577.1679_7.401 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.943901 | 23 | 0.0072900 | ** |
| 1578.1423_7.058 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.991713 | 23 | 0.0065200 | ** |
| 1578.1434_7.057 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.156550 | 23 | 0.0044100 | ** |
| 1578.178_7.924 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.009421 | 23 | 0.0005500 | *** |
| 1579.1825_7.633 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.464135 | 23 | 0.0216000 | * |
| 1579.649_7.139 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.878030 | 23 | 0.0007610 | *** |
| 1579.6508_7.286 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.277585 | 23 | 0.0324000 | * |
| 1580.1032_7.792 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.178222 | 23 | 0.0000300 | **** |
| 1580.1526_7.144 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.367702 | 23 | 0.0267000 | * |
| 1580.159_7.285 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.354848 | 23 | 0.0027400 | ** |
| 1581.1632_7.283 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.908718 | 23 | 0.0079100 | ** |
| 1581.2641_8.744 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.229460 | 23 | 0.0358000 | * |
| 1584.1871_8.061 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.720274 | 23 | 0.0122000 | * |
| 1585.1303_6.711 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.343413 | 23 | 0.0281000 | * |
| 1585.2008_8.971 | rel_abund_log2 | b1 | b3 | 24 | 24 | -8.307335 | 23 | 0.0000000 | **** |
| 1586.1131_6.864 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.012202 | 23 | 0.0062100 | ** |
| 1586.1251_6.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.076696 | 23 | 0.0492000 | * |
| 1586.2043_8.972 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.701546 | 23 | 0.0000001 | **** |
| 1588.1294_6.745 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.149570 | 23 | 0.0423000 | * |
| 1589.1303_6.863 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.663860 | 23 | 0.0139000 | * |
| 1590.1432_6.882 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.912075 | 23 | 0.0078500 | ** |
| 1590.143_7.137 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.131806 | 23 | 0.0046800 | ** |
| 1590.144_7.285 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.694116 | 23 | 0.0130000 | * |
| 1590.1749_7.958 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.642208 | 23 | 0.0013600 | ** |
| 1591.1476_7.288 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.150481 | 23 | 0.0423000 | * |
| 1591.1484_6.916 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.541557 | 23 | 0.0017400 | ** |
| 1591.1826_7.956 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.690026 | 23 | 0.0000086 | **** |
| 1591.2114_7.764 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.657099 | 23 | 0.0013100 | ** |
| 1592.1862_7.949 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.763217 | 23 | 0.0111000 | * |
| 1592.5935_6.867 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.258309 | 23 | 0.0034600 | ** |
| 1596.1232_6.714 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.538043 | 23 | 0.0184000 | * |
| 1596.195_7.999 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.091554 | 23 | 0.0477000 | * |
| 1596.3093_9.895 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.929112 | 23 | 0.0006710 | *** |
| 1596.6247_6.715 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.522720 | 23 | 0.0190000 | * |
| 1597.1201_6.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.716466 | 23 | 0.0123000 | * |
| 1597.2743_8.003 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.498598 | 23 | 0.0201000 | * |
| 1597.3131_9.894 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.903024 | 23 | 0.0000593 | **** |
| 1598.1451_7.197 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.081485 | 23 | 0.0004590 | *** |
| 160.0606_0.629 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.280060 | 23 | 0.0322000 | * |
| 1602.1428_8.354 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.064095 | 23 | 0.0055000 | ** |
| 1604.1571_7.283 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.110723 | 23 | 0.0459000 | * |
| 1604.1937_7.739 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.840929 | 23 | 0.0092600 | ** |
| 1604.1948_7.675 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.818001 | 23 | 0.0097600 | ** |
| 1605.1979_7.674 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.717053 | 23 | 0.0000942 | **** |
| 1609.235_8.392 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.517983 | 23 | 0.0018500 | ** |
| 1610.1103_6.827 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.104523 | 23 | 0.0049900 | ** |
| 1610.32_9.892 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.745850 | 23 | 0.0000877 | **** |
| 1611.1156_6.852 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.487472 | 23 | 0.0206000 | * |
| 1611.8348_9.901 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.194757 | 23 | 0.0385000 | * |
| 1616.16_7.754 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.503055 | 23 | 0.0019100 | ** |
| 1617.1626_7.754 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.091716 | 23 | 0.0004480 | *** |
| 1619.1782_7.285 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.651980 | 23 | 0.0142000 | * |
| 1619.2973_8.957 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.002752 | 23 | 0.0063500 | ** |
| 1621.1991_7.959 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.726045 | 23 | 0.0000921 | **** |
| 1621.2757_7.96 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.764232 | 23 | 0.0010100 | ** |
| 1622.2784_7.959 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.933736 | 23 | 0.0074600 | ** |
| 1623.2097_7.994 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.723922 | 23 | 0.0121000 | * |
| 1624.8418_9.901 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.025509 | 23 | 0.0060200 | ** |
| 1625.2172_8.052 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.449572 | 23 | 0.0223000 | * |
| 1625.2283_8.396 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.094942 | 23 | 0.0474000 | * |
| 1629.2009_7.725 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.490260 | 23 | 0.0019700 | ** |
| 1631.1866_7.959 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.710138 | 23 | 0.0011500 | ** |
| 1631.6823_7.958 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.223911 | 23 | 0.0037600 | ** |
| 1632.1881_7.956 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.243413 | 23 | 0.0035900 | ** |
| 1635.8345_9.901 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.611712 | 23 | 0.0156000 | * |
| 1636.3352_9.901 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.388666 | 23 | 0.0255000 | * |
| 1637.3363_9.913 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.667212 | 23 | 0.0138000 | * |
| 1642.1751_7.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.249353 | 23 | 0.0035400 | ** |
| 1643.1784_7.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.403435 | 23 | 0.0247000 | * |
| 1646.2057_7.997 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.233080 | 23 | 0.0003150 | *** |
| 1647.2105_8.001 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.601549 | 23 | 0.0015000 | ** |
| 1648.2179_8.411 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.277230 | 23 | 0.0324000 | * |
| 1648.3413_9.902 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.915324 | 23 | 0.0006940 | *** |
| 1649.3444_10.225 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.769806 | 23 | 0.0000071 | **** |
| 1649.3448_9.904 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.781220 | 23 | 0.0000803 | **** |
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| 1677.4008_11.264 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.972988 | 23 | 0.0068100 | ** |
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| 1681.1596_8.548 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.622537 | 23 | 0.0152000 | * |
| 188.0707_0.633 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.603118 | 23 | 0.0159000 | * |
| 205.1221_0.621 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.217807 | 23 | 0.0000024 | **** |
| 235.0925_0.646 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.720003 | 23 | 0.0011200 | ** |
| 247.1441_0.636 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.367875 | 23 | 0.0267000 | * |
| 286.1437_1.13 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.290922 | 23 | 0.0000002 | **** |
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| 293.0984_0.628 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.331508 | 23 | 0.0029000 | ** |
| 308.1855_0.633 | rel_abund_log2 | b1 | b3 | 24 | 24 | -16.437607 | 23 | 0.0000000 | **** |
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| 312.1597_1.413 | rel_abund_log2 | b1 | b3 | 24 | 24 | -8.957805 | 23 | 0.0000000 | **** |
| 315.0805_0.637 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.648465 | 23 | 0.0144000 | * |
| 316.2483_0.679 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.948590 | 23 | 0.0072100 | ** |
| 316.2608_0.679 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.047546 | 23 | 0.0057100 | ** |
| 328.2481_0.675 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.668168 | 23 | 0.0137000 | * |
| 331.0535_0.634 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.216703 | 23 | 0.0003280 | *** |
| 332.243_0.64 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.307170 | 23 | 0.0304000 | * |
| 337.1048_1.268 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.636525 | 23 | 0.0147000 | * |
| 342.2638_0.69 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.163273 | 23 | 0.0412000 | * |
| 344.2792_0.693 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.778688 | 23 | 0.0107000 | * |
| 344.2793_0.927 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.151382 | 23 | 0.0003860 | *** |
| 353.0342_0.629 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.102531 | 23 | 0.0467000 | * |
| 370.3549_12.075 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.965691 | 23 | 0.0069300 | ** |
| 373.2483_1.147 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.031820 | 23 | 0.0000004 | **** |
| 398.3413_0.639 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.567322 | 23 | 0.0172000 | * |
| 426.3575_2.274 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.997835 | 23 | 0.0064200 | ** |
| 428.3732_2.759 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.417857 | 23 | 0.0239000 | * |
| 438.2976_3.525 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.120010 | 23 | 0.0450000 | * |
| 464.1914_0.656 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.229234 | 23 | 0.0000024 | **** |
| 466.3289_4.123 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.428781 | 23 | 0.0234000 | * |
| 478.2926_2.855 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.194570 | 23 | 0.0386000 | * |
| 480.3082_3.357 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.578923 | 23 | 0.0000112 | **** |
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| 482.3238_2.701 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.087248 | 23 | 0.0481000 | * |
| 502.2902_3.355 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.551643 | 23 | 0.0001420 | *** |
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| 526.2644_1.714 | rel_abund_log2 | b1 | b3 | 24 | 24 | -11.468413 | 23 | 0.0000000 | **** |
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| 538.3859_4.051 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.047270 | 23 | 0.0005000 | *** |
| 542.3237_2.234 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.224065 | 23 | 0.0362000 | * |
| 543.4017_4.622 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.657994 | 23 | 0.0141000 | * |
| 544.3374_3.238 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.532066 | 23 | 0.0001490 | *** |
| 550.3862_3.859 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.398101 | 23 | 0.0024700 | ** |
| 552.4018_4.301 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.766431 | 23 | 0.0010000 | *** |
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| 563.4273_4.415 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.228972 | 23 | 0.0003180 | *** |
| 577.5185_8.358 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.183781 | 23 | 0.0000026 | **** |
| 578.3923_2.734 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.722901 | 23 | 0.0121000 | * |
| 593.4757_4.54 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.906852 | 23 | 0.0000001 | **** |
| 593.4762_4.644 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.988658 | 23 | 0.0000000 | **** |
| 593.4768_4.312 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.055884 | 23 | 0.0000406 | **** |
| 595.4929_4.645 | rel_abund_log2 | b1 | b3 | 24 | 24 | 9.813731 | 23 | 0.0000000 | **** |
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| 610.431_5.16 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.356891 | 23 | 0.0000193 | **** |
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| 610.5401_9.784 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.502733 | 23 | 0.0019200 | ** |
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| 619.4903_4.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | 11.942170 | 23 | 0.0000000 | **** |
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| 634.54_8.941 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.552931 | 23 | 0.0000011 | **** |
| 636.5558_9.845 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.504311 | 23 | 0.0198000 | * |
| 636.5558_9.995 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.532135 | 23 | 0.0186000 | * |
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| 650.6449_10.83 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.110892 | 23 | 0.0000354 | **** |
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| 691.5742_6.403 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.030814 | 23 | 0.0005210 | *** |
| 692.5581_7.191 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.417265 | 23 | 0.0240000 | * |
| 692.6335_11.899 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.190548 | 23 | 0.0003500 | *** |
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| 695.5093_5.094 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.337246 | 23 | 0.0028600 | ** |
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| 700.5274_7.902 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.584647 | 23 | 0.0166000 | * |
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| 717.5903_7.018 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.391203 | 23 | 0.0000178 | **** |
| 718.5742_8.138 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.785746 | 23 | 0.0009560 | *** |
| 718.5934_7.015 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.179711 | 23 | 0.0003600 | *** |
| 719.6006_7.392 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.406821 | 23 | 0.0000171 | **** |
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| 720.5897_8.289 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.533306 | 23 | 0.0186000 | * |
| 724.5277_7.806 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.923327 | 23 | 0.0006800 | *** |
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| 727.5745_6.067 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.036529 | 23 | 0.0005140 | *** |
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| 731.6047_7.112 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.382030 | 23 | 0.0259000 | * |
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| 732.589_8.53 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.746367 | 23 | 0.0000876 | **** |
| 733.6212_7.929 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.413026 | 23 | 0.0023800 | ** |
| 733.6215_7.929 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.397073 | 23 | 0.0024800 | ** |
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| 743.6054_7.227 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.536167 | 23 | 0.0000011 | **** |
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| 746.5685_9.239 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.042711 | 23 | 0.0005060 | *** |
| 746.6055_8.435 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.340698 | 23 | 0.0283000 | * |
| 748.5258_8.233 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.359582 | 23 | 0.0002300 | *** |
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| 748.5844_8.099 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.680982 | 23 | 0.0133000 | * |
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| 752.5592_8.969 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.382002 | 23 | 0.0025700 | ** |
| 752.5594_8.973 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.160124 | 23 | 0.0003780 | *** |
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| 754.5356_6.825 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.336753 | 23 | 0.0285000 | * |
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| 755.6054_6.838 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.047160 | 23 | 0.0000415 | **** |
| 756.5542_6.373 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.143416 | 23 | 0.0429000 | * |
| 756.5904_7.889 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.993224 | 23 | 0.0005720 | *** |
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| 759.1221_6.983 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.071162 | 23 | 0.0497000 | * |
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| 760.5281_6.984 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.323924 | 23 | 0.0293000 | * |
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| 760.5459_7.719 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.731704 | 23 | 0.0119000 | * |
| 760.5883_7.719 | rel_abund_log2 | b1 | b3 | 24 | 24 | 6.570374 | 23 | 0.0000010 | **** |
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| 761.2595_7.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.448148 | 23 | 0.0001840 | *** |
| 761.302_7.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.333808 | 23 | 0.0000205 | **** |
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| 761.3681_7.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.382642 | 23 | 0.0000181 | **** |
| 761.3781_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.262833 | 23 | 0.0002920 | *** |
| 761.4062_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.155622 | 23 | 0.0003820 | *** |
| 761.4141_7.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.068197 | 23 | 0.0004750 | *** |
| 761.4437_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.847667 | 23 | 0.0008210 | *** |
| 761.4545_7.719 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.045915 | 23 | 0.0000416 | **** |
| 761.5191_7.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.181053 | 23 | 0.0000298 | **** |
| 761.6526_9.142 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.572100 | 23 | 0.0170000 | * |
| 762.5032_7.719 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.594865 | 23 | 0.0000108 | **** |
| 762.5251_7.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.103061 | 23 | 0.0004350 | *** |
| 762.5433_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.808430 | 23 | 0.0000751 | **** |
| 762.6465_8.683 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.432602 | 23 | 0.0232000 | * |
| 764.5195_7.458 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.959266 | 23 | 0.0006220 | *** |
| 764.5566_7.52 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.584565 | 23 | 0.0166000 | * |
| 766.5713_8.323 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.619641 | 23 | 0.0014400 | ** |
| 766.5755_7.417 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.049141 | 23 | 0.0000004 | **** |
| 768.5536_6.434 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.786474 | 23 | 0.0000793 | **** |
| 768.5859_8.446 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.669412 | 23 | 0.0137000 | * |
| 768.587_8.451 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.336779 | 23 | 0.0002430 | *** |
| 768.5909_7.579 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.080306 | 23 | 0.0488000 | * |
| 770.0615_7.009 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.098340 | 23 | 0.0471000 | * |
| 770.5691_6.825 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.361826 | 23 | 0.0027000 | ** |
| 770.6056_8.664 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.680076 | 23 | 0.0134000 | * |
| 771.5762_7.712 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.527700 | 23 | 0.0001510 | *** |
| 771.6366_8.357 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.325112 | 23 | 0.0029500 | ** |
| 772.5245_7.894 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.574640 | 23 | 0.0169000 | * |
| 772.5855_7.501 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.724478 | 23 | 0.0121000 | * |
| 772.6196_8.643 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.422807 | 23 | 0.0023300 | ** |
| 773.6516_9.025 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.643316 | 23 | 0.0000009 | **** |
| 774.5401_8.626 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.205703 | 23 | 0.0377000 | * |
| 774.5404_8.982 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.863682 | 23 | 0.0007890 | *** |
| 775.6384_6.825 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.151765 | 23 | 0.0044600 | ** |
| 775.6661_9.814 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.487832 | 23 | 0.0001670 | *** |
| 776.579_6.552 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.408966 | 23 | 0.0244000 | * |
| 778.5355_6.376 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.088578 | 23 | 0.0480000 | * |
| 778.5376_5.842 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.703411 | 23 | 0.0127000 | * |
| 780.5519_6.84 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.042888 | 23 | 0.0057800 | ** |
| 780.5921_7.79 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.265133 | 23 | 0.0000022 | **** |
| 781.6192_8.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.235728 | 23 | 0.0003130 | *** |
| 781.62_7.043 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.462174 | 23 | 0.0000014 | **** |
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| 783.332_6.875 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.224280 | 23 | 0.0362000 | * |
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| 783.6368_7.847 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.635750 | 23 | 0.0000009 | **** |
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| 784.6308_6.874 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.276756 | 23 | 0.0324000 | * |
| 784.6654_10.38 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.641168 | 23 | 0.0000097 | **** |
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| 784.7847_6.875 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.543744 | 23 | 0.0181000 | * |
| 785.0064_6.878 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.729866 | 23 | 0.0119000 | * |
| 785.0201_6.879 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.162337 | 23 | 0.0043500 | ** |
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| 785.6536_8.747 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.369686 | 23 | 0.0002240 | *** |
| 785.6542_8.953 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.422767 | 23 | 0.0001960 | *** |
| 786.5814_6.876 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.989887 | 23 | 0.0065400 | ** |
| 786.6508_7.289 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.150284 | 23 | 0.0423000 | * |
| 787.6674_9.202 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.596889 | 23 | 0.0000001 | **** |
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| 788.5563_7.415 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.322305 | 23 | 0.0000019 | **** |
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| 790.5352_6.424 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.996956 | 23 | 0.0064400 | ** |
| 790.5744_7.193 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.718453 | 23 | 0.0000939 | **** |
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| 791.5433_6.743 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.075631 | 23 | 0.0493000 | * |
| 792.5512_6.827 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.311696 | 23 | 0.0301000 | * |
| 792.5531_6.261 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.837824 | 23 | 0.0008410 | *** |
| 792.5873_8.665 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.389761 | 23 | 0.0254000 | * |
| 792.5893_7.058 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.618491 | 23 | 0.0014400 | ** |
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| 793.556_6.829 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.157729 | 23 | 0.0416000 | * |
| 794.5862_7.544 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.761681 | 23 | 0.0010100 | ** |
| 794.6054_8.214 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.139427 | 23 | 0.0003980 | *** |
| 794.6057_8.544 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.307904 | 23 | 0.0030700 | ** |
| 794.6868_10.627 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.338175 | 23 | 0.0284000 | * |
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| 796.5845_7.27 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.361776 | 23 | 0.0002290 | *** |
| 796.5848_7.423 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.673861 | 23 | 0.0000008 | **** |
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| 798.6518_8.391 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.067732 | 23 | 0.0000003 | **** |
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| 799.6069_8.865 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.592181 | 23 | 0.0015400 | ** |
| 799.6686_9.32 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.905750 | 23 | 0.0000051 | **** |
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| 800.5726_6.827 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.221972 | 23 | 0.0364000 | * |
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| 800.616_8.426 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.331423 | 23 | 0.0289000 | * |
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| 802.5717_7.81 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.351264 | 23 | 0.0002350 | *** |
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| 804.5937_7.544 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.908953 | 23 | 0.0007050 | *** |
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| 805.6187_7.848 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.468388 | 23 | 0.0000147 | **** |
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| 806.5698_6.37 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.111588 | 23 | 0.0004260 | *** |
| 806.6235_6.835 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.233371 | 23 | 0.0355000 | * |
| 807.6349_8.948 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.137075 | 23 | 0.0000003 | **** |
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| 810.6564_8.03 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.706567 | 23 | 0.0000082 | **** |
| 810.6812_10.387 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.748138 | 23 | 0.0010500 | ** |
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| 811.4149_7.961 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.195496 | 23 | 0.0040200 | ** |
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| 811.6292_7.819 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.538839 | 23 | 0.0000124 | **** |
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| 811.6703_8.961 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.376785 | 23 | 0.0002200 | *** |
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| 812.6186_8.396 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.112807 | 23 | 0.0457000 | * |
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| 813.6834_9.437 | rel_abund_log2 | b1 | b3 | 24 | 24 | -8.493589 | 23 | 0.0000000 | **** |
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| 820.5846_7.213 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.247673 | 23 | 0.0000253 | **** |
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| 820.6961_11.574 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.238175 | 23 | 0.0000023 | **** |
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| 824.616_8.544 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.304151 | 23 | 0.0002640 | *** |
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| 825.6241_9.886 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.819372 | 23 | 0.0008800 | *** |
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| 825.6815_9.335 | rel_abund_log2 | b1 | b3 | 24 | 24 | -12.432634 | 23 | 0.0000000 | **** |
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| 826.5716_8.868 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.005861 | 23 | 0.0063000 | ** |
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| 827.6999_10.389 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.648085 | 23 | 0.0000095 | **** |
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| 832.5817_7.451 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.726347 | 23 | 0.0120000 | * |
| 832.582_7.631 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.173864 | 23 | 0.0000027 | **** |
| 833.6507_8.971 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.321200 | 23 | 0.0029700 | ** |
| 834.5987_8.394 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.513913 | 23 | 0.0018600 | ** |
| 834.6002_7.257 | rel_abund_log2 | b1 | b3 | 24 | 24 | -6.625355 | 23 | 0.0000009 | **** |
| 834.602_7.751 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.454234 | 23 | 0.0021600 | ** |
| 835.5327_7.555 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.163889 | 23 | 0.0411000 | * |
| 835.6566_8.968 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.400271 | 23 | 0.0249000 | * |
| 835.6663_8.407 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.447117 | 23 | 0.0001850 | *** |
| 835.6664_10.22 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.234571 | 23 | 0.0000261 | **** |
| 835.6666_9.91 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.434559 | 23 | 0.0000160 | **** |
| 836.6136_9.086 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.358852 | 23 | 0.0272000 | * |
| 836.6148_8.439 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.124144 | 23 | 0.0047700 | ** |
| 836.6167_8.053 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.099029 | 23 | 0.0470000 | * |
| 837.5477_8.363 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.192337 | 23 | 0.0000003 | **** |
| 837.614_7.764 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.419908 | 23 | 0.0023400 | ** |
| 837.6724_9.906 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.650800 | 23 | 0.0143000 | * |
| 837.6789_10.324 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.803712 | 23 | 0.0009150 | *** |
| 837.6818_10.567 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.797581 | 23 | 0.0102000 | * |
| 837.6831_9.217 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.730008 | 23 | 0.0011000 | ** |
| 838.6301_9.147 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.610578 | 23 | 0.0156000 | * |
| 838.6321_8.779 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.243784 | 23 | 0.0035800 | ** |
| 839.6989_10.178 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.180361 | 23 | 0.0003590 | *** |
| 840.5868_7.472 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.394797 | 23 | 0.0252000 | * |
| 842.5663_7.213 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.461926 | 23 | 0.0021200 | ** |
| 842.723_11.155 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.988176 | 23 | 0.0005790 | *** |
| 844.7397_11.265 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.789557 | 23 | 0.0009470 | *** |
| 846.6641_8.878 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.066952 | 23 | 0.0054600 | ** |
| 847.6893_7.716 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.436797 | 23 | 0.0001900 | *** |
| 848.6521_9.028 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.790473 | 23 | 0.0000001 | **** |
| 849.6682_6.746 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.947365 | 23 | 0.0072300 | ** |
| 849.6939_11.262 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.006961 | 23 | 0.0062900 | ** |
| 851.64_9.902 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.694864 | 23 | 0.0012000 | ** |
| 852.5508_6.228 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.348131 | 23 | 0.0278000 | * |
| 852.6843_10.599 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.423696 | 23 | 0.0236000 | * |
| 853.6786_8.974 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.907178 | 23 | 0.0007080 | *** |
| 853.6786_8.975 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.441902 | 23 | 0.0022200 | ** |
| 854.574_8.362 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.755834 | 23 | 0.0000073 | **** |
| 856.5819_7.24 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.296024 | 23 | 0.0000225 | **** |
| 856.5823_7.778 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.480786 | 23 | 0.0001700 | *** |
| 858.5914_7.792 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.482889 | 23 | 0.0001690 | *** |
| 858.5977_8.453 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.839074 | 23 | 0.0008380 | *** |
| 858.5992_7.06 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.422044 | 23 | 0.0001970 | *** |
| 858.7545_11.345 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.964898 | 23 | 0.0000509 | **** |
| 859.53_8.35 | rel_abund_log2 | b1 | b3 | 24 | 24 | 8.289635 | 23 | 0.0000000 | **** |
| 859.6891_7.519 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.048987 | 23 | 0.0004980 | *** |
| 860.6131_9.152 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.187421 | 23 | 0.0391000 | * |
| 860.6133_8.785 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.395073 | 23 | 0.0002100 | *** |
| 860.7704_11.477 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.620741 | 23 | 0.0153000 | * |
| 861.7048_7.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.279419 | 23 | 0.0002810 | *** |
| 862.6306_8.44 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.115622 | 23 | 0.0454000 | * |
| 863.6838_8.874 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.207465 | 23 | 0.0039100 | ** |
| 863.7092_11.343 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.570092 | 23 | 0.0016200 | ** |
| 863.7199_8.448 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.201969 | 23 | 0.0380000 | * |
| 865.5795_9.626 | rel_abund_log2 | b1 | b3 | 24 | 24 | 7.826774 | 23 | 0.0000001 | **** |
| 866.723_11.11 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.057584 | 23 | 0.0055800 | ** |
| 868.7389_11.19 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.654187 | 23 | 0.0001100 | *** |
| 869.6734_6.444 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.123826 | 23 | 0.0447000 | * |
| 869.6738_6.894 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.665499 | 23 | 0.0138000 | * |
| 870.6336_9.039 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.900536 | 23 | 0.0000051 | **** |
| 870.7558_11.296 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.608460 | 23 | 0.0000104 | **** |
| 871.6776_11.111 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.323479 | 23 | 0.0029600 | ** |
| 871.6892_7.141 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.508699 | 23 | 0.0018900 | ** |
| 872.6526_8.477 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.873664 | 23 | 0.0000638 | **** |
| 872.652_8.777 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.880113 | 23 | 0.0000054 | **** |
| 872.7324_11.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.282385 | 23 | 0.0320000 | * |
| 872.7731_11.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.209801 | 23 | 0.0000278 | **** |
| 873.7045_7.52 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.726576 | 23 | 0.0000920 | **** |
| 874.5538_7.197 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.489022 | 23 | 0.0205000 | * |
| 874.6655_10.042 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.831711 | 23 | 0.0008540 | *** |
| 874.7461_11.559 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.256692 | 23 | 0.0338000 | * |
| 874.7892_11.559 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.341595 | 23 | 0.0282000 | * |
| 875.7095_11.292 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.684946 | 23 | 0.0000087 | **** |
| 875.7207_8.87 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.517768 | 23 | 0.0018500 | ** |
| 876.5695_7.88 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.821973 | 23 | 0.0000726 | **** |
| 876.5697_8.13 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.496049 | 23 | 0.0202000 | * |
| 876.6835_10.245 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.038953 | 23 | 0.0000004 | **** |
| 877.6374_9.86 | rel_abund_log2 | b1 | b3 | 24 | 24 | 5.198234 | 23 | 0.0000286 | **** |
| 877.6374_9.988 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.527718 | 23 | 0.0018000 | ** |
| 877.7253_11.418 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.447008 | 23 | 0.0225000 | * |
| 877.7_7.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.888769 | 23 | 0.0007410 | *** |
| 878.5852_8.857 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.678875 | 23 | 0.0012400 | ** |
| 878.7033_7.717 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.397711 | 23 | 0.0002090 | *** |
| 879.7408_11.578 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.194336 | 23 | 0.0386000 | * |
| 880.718_11.248 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.376792 | 23 | 0.0002200 | *** |
| 881.6374_6.724 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.197603 | 23 | 0.0383000 | * |
| 883.6892_7.419 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.528574 | 23 | 0.0000001 | **** |
| 883.6894_6.894 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.529922 | 23 | 0.0017900 | ** |
| 884.7697_11.358 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.637726 | 23 | 0.0001150 | *** |
| 886.7858_11.492 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.346488 | 23 | 0.0002370 | *** |
| 887.6837_6.371 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.069430 | 23 | 0.0499000 | * |
| 887.7201_8.662 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.531450 | 23 | 0.0187000 | * |
| 889.6375_9.586 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.249721 | 23 | 0.0343000 | * |
| 889.6996_7.495 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.497189 | 23 | 0.0201000 | * |
| 889.7358_8.865 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.108084 | 23 | 0.0049500 | ** |
| 891.7156_7.718 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.507412 | 23 | 0.0001590 | *** |
| 891.7406_11.489 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.703443 | 23 | 0.0011700 | ** |
| 892.7383_11.126 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.555903 | 23 | 0.0016800 | ** |
| 893.6733_6.713 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.456593 | 23 | 0.0220000 | * |
| 893.7563_11.645 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.429985 | 23 | 0.0022900 | ** |
| 894.7548_11.21 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.490365 | 23 | 0.0001660 | *** |
| 896.5379_6.373 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.425185 | 23 | 0.0236000 | * |
| 896.7714_11.316 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.704403 | 23 | 0.0000972 | **** |
| 897.7047_7.419 | rel_abund_log2 | b1 | b3 | 24 | 24 | -7.790842 | 23 | 0.0000001 | **** |
| 897.7049_6.893 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.817656 | 23 | 0.0097700 | ** |
| 897.705_7.958 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.277787 | 23 | 0.0033000 | ** |
| 898.7872_11.44 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.973604 | 23 | 0.0068000 | ** |
| 899.684_6.44 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.228754 | 23 | 0.0359000 | * |
| 899.7091_11.203 | rel_abund_log2 | b1 | b3 | 24 | 24 | -5.540967 | 23 | 0.0000123 | **** |
| 899.7204_7.586 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.377235 | 23 | 0.0261000 | * |
| 899.7208_8.396 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.562957 | 23 | 0.0174000 | * |
| 900.6836_9.96 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.653275 | 23 | 0.0001100 | *** |
| 901.6999_7.143 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.613462 | 23 | 0.0001220 | *** |
| 901.7251_11.318 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.802022 | 23 | 0.0009190 | *** |
| 902.5743_7.805 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.419880 | 23 | 0.0238000 | * |
| 902.7673_8.957 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.294921 | 23 | 0.0031700 | ** |
| 902.8182_11.738 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.055876 | 23 | 0.0056000 | ** |
| 903.6531_10.207 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.614281 | 23 | 0.0155000 | * |
| 903.6531_9.918 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.688009 | 23 | 0.0012200 | ** |
| 904.7835_9.894 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.684184 | 23 | 0.0132000 | * |
| 904.8336_11.91 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.839866 | 23 | 0.0008370 | *** |
| 907.6886_7.2 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.678294 | 23 | 0.0134000 | * |
| 907.7722_11.767 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.145514 | 23 | 0.0427000 | * |
| 911.5614_9.119 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.453120 | 23 | 0.0222000 | * |
| 911.7203_7.674 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.882662 | 23 | 0.0084000 | ** |
| 911.7206_7.958 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.466535 | 23 | 0.0020900 | ** |
| 912.8727_11.58 | rel_abund_log2 | b1 | b3 | 24 | 24 | 4.574625 | 23 | 0.0001340 | *** |
| 913.7362_8.395 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.850891 | 23 | 0.0090400 | ** |
| 915.7154_7.143 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.833706 | 23 | 0.0008490 | *** |
| 916.7378_11.078 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.613855 | 23 | 0.0155000 | * |
| 918.754_11.178 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.659181 | 23 | 0.0013100 | ** |
| 918.7987_9.897 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.056147 | 23 | 0.0056000 | ** |
| 919.7465_8.87 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.667840 | 23 | 0.0012800 | ** |
| 920.75_8.87 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.630550 | 23 | 0.0149000 | * |
| 920.7705_11.302 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.692494 | 23 | 0.0130000 | * |
| 922.7862_11.386 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.565127 | 23 | 0.0016400 | ** |
| 923.684_6.374 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.056209 | 23 | 0.0056000 | ** |
| 923.7092_11.174 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.676132 | 23 | 0.0135000 | * |
| 924.8016_11.511 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.657048 | 23 | 0.0013100 | ** |
| 926.8159_11.647 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.479754 | 23 | 0.0209000 | * |
| 927.7399_11.374 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.413198 | 23 | 0.0242000 | * |
| 928.7831_8.964 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.885772 | 23 | 0.0007470 | *** |
| 929.7558_11.499 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.417969 | 23 | 0.0239000 | * |
| 930.7988_10.228 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.246294 | 23 | 0.0035600 | ** |
| 930.7989_9.903 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.045202 | 23 | 0.0057500 | ** |
| 930.8479_11.919 | rel_abund_log2 | b1 | b3 | 24 | 24 | 3.548618 | 23 | 0.0017100 | ** |
| 931.7715_11.646 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.509229 | 23 | 0.0018900 | ** |
| 937.6995_6.371 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.746088 | 23 | 0.0000877 | **** |
| 938.8159_11.193 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.654542 | 23 | 0.0001100 | *** |
| 941.7308_7.633 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.652261 | 23 | 0.0013300 | ** |
| 941.7313_7.957 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.813549 | 23 | 0.0008930 | *** |
| 942.7538_11.123 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.402254 | 23 | 0.0248000 | * |
| 942.7986_8.961 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.410714 | 23 | 0.0243000 | * |
| 944.7698_11.221 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.344451 | 23 | 0.0028100 | ** |
| 944.8145_9.903 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.171629 | 23 | 0.0042600 | ** |
| 944.8656_11.558 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.367638 | 23 | 0.0267000 | * |
| 949.7248_11.221 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.237497 | 23 | 0.0036400 | ** |
| 950.8156_11.512 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.303901 | 23 | 0.0031000 | ** |
| 951.7153_7.77 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.845491 | 23 | 0.0008250 | *** |
| 952.831_11.66 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.636550 | 23 | 0.0147000 | * |
| 953.7312_8.446 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.275111 | 23 | 0.0325000 | * |
| 953.7558_11.474 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.402817 | 23 | 0.0247000 | * |
| 955.7465_8.781 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.389033 | 23 | 0.0025200 | ** |
| 957.7865_11.655 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.544370 | 23 | 0.0181000 | * |
| 963.8345_12.135 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.395330 | 23 | 0.0251000 | * |
| 965.7308_7.761 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.527716 | 23 | 0.0018000 | ** |
| 966.8463_11.305 | rel_abund_log2 | b1 | b3 | 24 | 24 | -3.170502 | 23 | 0.0042700 | ** |
| 968.863_11.436 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.230263 | 23 | 0.0358000 | * |
| 972.7339_9.773 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.710553 | 23 | 0.0125000 | * |
| 972.8_11.345 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.649783 | 23 | 0.0143000 | * |
| 976.684_9.115 | rel_abund_log2 | b1 | b3 | 24 | 24 | 2.333113 | 23 | 0.0287000 | * |
| 978.8456_11.66 | rel_abund_log2 | b1 | b3 | 24 | 24 | -2.233451 | 23 | 0.0355000 | * |
| 993.7984_10.238 | rel_abund_log2 | b1 | b3 | 24 | 24 | -4.313462 | 23 | 0.0002580 | *** |
## [1] 786
# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_tomANDctrl <- full_join(tomato_t.test_paired_sig, ctrl_t.test_paired_sig,
by = "mz_rt",
suffix = c(".tom", ".ctrl"))
# number features in full list
nrow(fulljoin_t.test_tomANDctrl)## [1] 897
Let’s try and remove features significant due to the background diet
sig_paired_tom_rmBG <- fulljoin_t.test_tomANDctrl %>%
# add a column to account for direction
mutate(sign = statistic.tom * statistic.ctrl) %>%
# replace NAs in the sign column with 0
mutate(sign = replace_na(sign, 0)) %>%
# replace NAs in the statistic.ctrl column to 0
mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
# filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
filter((sign < 0 | statistic.ctrl == 0))
# number of features in new list without bg diet effect
nrow(sig_paired_tom_rmBG)## [1] 460
How many BG-diet-related features did we remove from the list of significant beta features?
## [1] 326
Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)
# select only features from paired list that have a match in ANOVA list
tom_sig_ANOVA_overlap_paired <- inner_join(sig_paired_tom_rmBG,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# features overlapping with sig ANOVA
unique(tom_sig_ANOVA_overlap_paired$mz_rt)## [1] "1157.3434_6.852" "1341.2177_11.895" "1342.2211_11.896" "1479.1185_7.024"
## [5] "1526.0908_8.976" "1527.094_8.977" "1536.6252_6.856" "1619.2973_8.957"
## [9] "1624.8418_9.901" "1635.8345_9.901" "1649.3448_9.904" "286.2013_0.633"
## [13] "337.1048_1.268" "536.437_11.201" "605.5497_9.629" "608.4637_5.649"
## [17] "677.5586_5.973" "726.5428_8.237" "738.543_8.181" "748.5258_8.233"
## [21] "759.0903_6.983" "759.1221_6.983" "774.5404_8.982" "785.0201_6.879"
## [25] "796.5222_9.007" "797.6453_8.401" "811.6598_8.009" "813.6866_9.902"
## [29] "827.6999_10.389" "835.6566_8.968" "851.64_9.902" "865.5795_9.626"
## [33] "876.5695_7.88" "878.5852_8.857" "889.6375_9.586" "897.705_7.958"
## [37] "903.6531_10.207" "907.7722_11.767"
Here, I will compare control to each tomato treatment individually, and then tomato treatments against each other. I will also compare tomato to control. I am using the log transformed values of rel abundance since parametric tests assume normality.
# run t-test
red_v_ctrl_t.test <- df_for_stats %>%
filter(treatment %in% c("control", "red")) %>%
filter(period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_red_v_ctrl_t.test <- red_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_red_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1186.3509_7.119 | rel_abund_log2 | control | red | 11 | 12 | -2.127690 | 20.99967 | 0.04540 | * |
| 1226.4736_8.96 | rel_abund_log2 | control | red | 11 | 12 | 2.915862 | 20.99028 | 0.00826 | ** |
| 1329.2228_12.335 | rel_abund_log2 | control | red | 11 | 12 | 2.228528 | 19.22203 | 0.03800 | * |
| 1522.0815_7.299 | rel_abund_log2 | control | red | 11 | 12 | -2.202931 | 20.08307 | 0.03940 | * |
| 1551.6195_6.93 | rel_abund_log2 | control | red | 11 | 12 | -2.158220 | 20.99914 | 0.04260 | * |
| 1552.6273_7.034 | rel_abund_log2 | control | red | 11 | 12 | 2.245082 | 17.03950 | 0.03830 | * |
| 1594.0976_6.869 | rel_abund_log2 | control | red | 11 | 12 | 2.113064 | 20.98841 | 0.04670 | * |
| 1595.6827_8.019 | rel_abund_log2 | control | red | 11 | 12 | -2.611962 | 20.65333 | 0.01640 | * |
| 1597.3162_8.956 | rel_abund_log2 | control | red | 11 | 12 | 2.147835 | 20.96746 | 0.04360 | * |
| 1607.1867_8.003 | rel_abund_log2 | control | red | 11 | 12 | -2.805197 | 20.96735 | 0.01060 | * |
| 1612.335_9.9 | rel_abund_log2 | control | red | 11 | 12 | 2.328866 | 20.79227 | 0.03000 | * |
| 162.1126_0.545 | rel_abund_log2 | control | red | 11 | 12 | 3.511107 | 20.93913 | 0.00208 | ** |
| 1685.4637_11.522 | rel_abund_log2 | control | red | 11 | 12 | -2.617311 | 19.45746 | 0.01670 | * |
| 184.0946_0.54 | rel_abund_log2 | control | red | 11 | 12 | 2.721035 | 20.57593 | 0.01290 | * |
| 330.2637_0.689 | rel_abund_log2 | control | red | 11 | 12 | -2.130643 | 16.71068 | 0.04830 | * |
| 369.3508_10.879 | rel_abund_log2 | control | red | 11 | 12 | 2.499518 | 20.72949 | 0.02090 | * |
| 383.1533_0.931 | rel_abund_log2 | control | red | 11 | 12 | -2.516115 | 16.30960 | 0.02270 | * |
| 515.313_3.077 | rel_abund_log2 | control | red | 11 | 12 | 2.379443 | 19.73893 | 0.02750 | * |
| 536.437_10.971 | rel_abund_log2 | control | red | 11 | 12 | -17.577022 | 20.56390 | 0.00000 | **** |
| 698.6194_10.879 | rel_abund_log2 | control | red | 11 | 12 | 2.818125 | 15.64467 | 0.01260 | * |
| 744.5538_6.51 | rel_abund_log2 | control | red | 11 | 12 | -2.190512 | 17.81496 | 0.04200 | * |
| 759.1449_6.983 | rel_abund_log2 | control | red | 11 | 12 | -3.104064 | 20.80020 | 0.00541 | ** |
| 759.243_6.983 | rel_abund_log2 | control | red | 11 | 12 | -2.114587 | 20.52142 | 0.04690 | * |
| 759.2652_6.984 | rel_abund_log2 | control | red | 11 | 12 | -2.202204 | 20.68103 | 0.03910 | * |
| 759.4259_6.982 | rel_abund_log2 | control | red | 11 | 12 | -2.177128 | 20.98581 | 0.04100 | * |
| 760.4052_6.983 | rel_abund_log2 | control | red | 11 | 12 | -2.309024 | 15.84633 | 0.03480 | * |
| 760.4925_6.98 | rel_abund_log2 | control | red | 11 | 12 | -3.003788 | 20.02325 | 0.00701 | ** |
| 774.5634_4.81 | rel_abund_log2 | control | red | 11 | 12 | -3.173353 | 20.99943 | 0.00458 | ** |
| 783.431_6.876 | rel_abund_log2 | control | red | 11 | 12 | 2.162299 | 19.61778 | 0.04310 | * |
| 784.6324_6.878 | rel_abund_log2 | control | red | 11 | 12 | 2.312621 | 12.94912 | 0.03780 | * |
| 787.0988_8.052 | rel_abund_log2 | control | red | 11 | 12 | -2.254153 | 20.96946 | 0.03500 | * |
| 793.5586_7.102 | rel_abund_log2 | control | red | 11 | 12 | 2.085902 | 20.49242 | 0.04970 | * |
| 797.5914_8.07 | rel_abund_log2 | control | red | 11 | 12 | -2.487848 | 15.23107 | 0.02490 | * |
| 826.594_5.988 | rel_abund_log2 | control | red | 11 | 12 | -3.865591 | 15.83786 | 0.00139 | ** |
| 836.6167_8.053 | rel_abund_log2 | control | red | 11 | 12 | 2.348559 | 20.98178 | 0.02870 | * |
| 844.6529_8.109 | rel_abund_log2 | control | red | 11 | 12 | -3.094096 | 17.15228 | 0.00653 | ** |
| 928.7831_8.964 | rel_abund_log2 | control | red | 11 | 12 | 2.129956 | 20.87973 | 0.04520 | * |
## [1] 37
Keep sig features in unpaired t-test that have a match in sig ANOVA
sig_overlap_ctrl_red <- inner_join(sig_red_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
unique(sig_overlap_ctrl_red$mz_rt)## [1] "1226.4736_8.96" "1594.0976_6.869" "162.1126_0.545" "536.437_10.971"
## [5] "797.5914_8.07" "928.7831_8.964"
# run t-tests
beta_v_ctrl_t.test <- df_for_stats %>%
filter(treatment %in% c("control" , "beta"),
period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_beta_v_ctrl_t.test <- beta_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_beta_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1002.658_3.079 | rel_abund_log2 | beta | control | 12 | 11 | 2.192276 | 20.99753 | 0.0398000 | * |
| 118.0864_0.597 | rel_abund_log2 | beta | control | 12 | 11 | -2.152878 | 17.28673 | 0.0457000 | * |
| 1192.8412_6.709 | rel_abund_log2 | beta | control | 12 | 11 | -2.096670 | 19.71279 | 0.0491000 | * |
| 1193.3431_6.71 | rel_abund_log2 | beta | control | 12 | 11 | -2.706443 | 20.99265 | 0.0132000 | * |
| 1195.8336_6.857 | rel_abund_log2 | beta | control | 12 | 11 | -2.157819 | 20.46148 | 0.0430000 | * |
| 1197.3456_7.08 | rel_abund_log2 | beta | control | 12 | 11 | -2.397133 | 19.05306 | 0.0269000 | * |
| 1218.5051_9.902 | rel_abund_log2 | beta | control | 12 | 11 | -2.670035 | 17.96773 | 0.0156000 | * |
| 1226.4736_8.96 | rel_abund_log2 | beta | control | 12 | 11 | -3.010350 | 18.94948 | 0.0072100 | ** |
| 1227.3893_7.956 | rel_abund_log2 | beta | control | 12 | 11 | -2.771515 | 19.77305 | 0.0119000 | * |
| 1227.7565_5.834 | rel_abund_log2 | beta | control | 12 | 11 | -2.746757 | 19.42664 | 0.0127000 | * |
| 1242.5051_10.22 | rel_abund_log2 | beta | control | 12 | 11 | -2.152101 | 18.18360 | 0.0451000 | * |
| 1242.5053_9.906 | rel_abund_log2 | beta | control | 12 | 11 | -2.450448 | 19.02528 | 0.0241000 | * |
| 1329.2228_12.335 | rel_abund_log2 | beta | control | 12 | 11 | -2.987544 | 19.54171 | 0.0074100 | ** |
| 1406.1439_6.504 | rel_abund_log2 | beta | control | 12 | 11 | -2.333209 | 18.30912 | 0.0312000 | * |
| 1417.6332_6.508 | rel_abund_log2 | beta | control | 12 | 11 | -2.444258 | 14.34033 | 0.0280000 | * |
| 1418.1346_6.507 | rel_abund_log2 | beta | control | 12 | 11 | -2.664070 | 17.33629 | 0.0162000 | * |
| 1419.1345_6.509 | rel_abund_log2 | beta | control | 12 | 11 | -2.238653 | 17.49860 | 0.0384000 | * |
| 1429.1272_6.513 | rel_abund_log2 | beta | control | 12 | 11 | -2.465973 | 20.97621 | 0.0224000 | * |
| 1548.6249_6.858 | rel_abund_log2 | beta | control | 12 | 11 | -2.796636 | 18.23823 | 0.0118000 | * |
| 1557.1328_6.71 | rel_abund_log2 | beta | control | 12 | 11 | -2.970215 | 20.99902 | 0.0073000 | ** |
| 1558.1357_6.713 | rel_abund_log2 | beta | control | 12 | 11 | -2.224426 | 20.05466 | 0.0378000 | * |
| 1575.1173_6.863 | rel_abund_log2 | beta | control | 12 | 11 | -2.439213 | 18.78736 | 0.0248000 | * |
| 1577.1237_6.867 | rel_abund_log2 | beta | control | 12 | 11 | -2.162239 | 20.30340 | 0.0427000 | * |
| 1585.1303_6.711 | rel_abund_log2 | beta | control | 12 | 11 | -2.322577 | 18.17618 | 0.0320000 | * |
| 1592.2118_7.764 | rel_abund_log2 | beta | control | 12 | 11 | -2.427547 | 20.44978 | 0.0245000 | * |
| 1596.1232_6.714 | rel_abund_log2 | beta | control | 12 | 11 | -2.267284 | 15.09253 | 0.0385000 | * |
| 1596.6247_6.715 | rel_abund_log2 | beta | control | 12 | 11 | -2.352786 | 14.42196 | 0.0333000 | * |
| 1597.1201_6.716 | rel_abund_log2 | beta | control | 12 | 11 | -2.098926 | 18.79358 | 0.0496000 | * |
| 1597.3162_8.956 | rel_abund_log2 | beta | control | 12 | 11 | -2.595291 | 19.82295 | 0.0174000 | * |
| 1601.3486_9.899 | rel_abund_log2 | beta | control | 12 | 11 | -2.189008 | 16.42417 | 0.0434000 | * |
| 1611.8348_9.901 | rel_abund_log2 | beta | control | 12 | 11 | -2.107998 | 17.87676 | 0.0494000 | * |
| 1612.335_9.9 | rel_abund_log2 | beta | control | 12 | 11 | -2.392405 | 17.24316 | 0.0284000 | * |
| 1618.2939_8.956 | rel_abund_log2 | beta | control | 12 | 11 | -3.352964 | 20.92152 | 0.0030200 | ** |
| 1619.2973_8.957 | rel_abund_log2 | beta | control | 12 | 11 | -2.874547 | 19.99275 | 0.0093700 | ** |
| 1624.3414_10.412 | rel_abund_log2 | beta | control | 12 | 11 | -2.169675 | 20.82741 | 0.0418000 | * |
| 1624.8418_9.901 | rel_abund_log2 | beta | control | 12 | 11 | -2.283605 | 17.54810 | 0.0351000 | * |
| 1635.8345_9.901 | rel_abund_log2 | beta | control | 12 | 11 | -2.270944 | 15.87441 | 0.0374000 | * |
| 536.437_10.971 | rel_abund_log2 | beta | control | 12 | 11 | 3.020418 | 20.87237 | 0.0065400 | ** |
| 536.437_11.201 | rel_abund_log2 | beta | control | 12 | 11 | 7.859672 | 14.28943 | 0.0000015 | **** |
| 568.4266_4.64 | rel_abund_log2 | beta | control | 12 | 11 | -2.922333 | 20.86424 | 0.0081800 | ** |
| 569.4277_4.639 | rel_abund_log2 | beta | control | 12 | 11 | -2.934578 | 19.21458 | 0.0084400 | ** |
| 585.2705_0.643 | rel_abund_log2 | beta | control | 12 | 11 | -2.554432 | 20.85213 | 0.0185000 | * |
| 593.4762_4.644 | rel_abund_log2 | beta | control | 12 | 11 | -2.605201 | 19.61083 | 0.0171000 | * |
| 614.3398_3.784 | rel_abund_log2 | beta | control | 12 | 11 | 2.197436 | 20.98167 | 0.0394000 | * |
| 692.5581_7.191 | rel_abund_log2 | beta | control | 12 | 11 | -2.305604 | 20.72998 | 0.0316000 | * |
| 701.5604_5.818 | rel_abund_log2 | beta | control | 12 | 11 | -2.101908 | 20.98834 | 0.0478000 | * |
| 703.5745_6.822 | rel_abund_log2 | beta | control | 12 | 11 | -2.205941 | 19.68302 | 0.0394000 | * |
| 703.5779_6.502 | rel_abund_log2 | beta | control | 12 | 11 | -2.176357 | 17.33686 | 0.0436000 | * |
| 704.3938_6.501 | rel_abund_log2 | beta | control | 12 | 11 | -2.164504 | 17.94508 | 0.0442000 | * |
| 704.4038_6.503 | rel_abund_log2 | beta | control | 12 | 11 | -2.785378 | 17.21802 | 0.0126000 | * |
| 705.6376_6.502 | rel_abund_log2 | beta | control | 12 | 11 | -2.295174 | 15.32628 | 0.0362000 | * |
| 725.5564_6.829 | rel_abund_log2 | beta | control | 12 | 11 | -2.341706 | 16.94685 | 0.0317000 | * |
| 741.551_6.153 | rel_abund_log2 | beta | control | 12 | 11 | -2.445215 | 20.92336 | 0.0234000 | * |
| 742.5718_8.28 | rel_abund_log2 | beta | control | 12 | 11 | -2.215412 | 20.62847 | 0.0381000 | * |
| 751.5717_6.812 | rel_abund_log2 | beta | control | 12 | 11 | -2.106329 | 20.70189 | 0.0476000 | * |
| 753.5881_7.505 | rel_abund_log2 | beta | control | 12 | 11 | -2.382275 | 20.14697 | 0.0272000 | * |
| 765.9874_3.073 | rel_abund_log2 | beta | control | 12 | 11 | 2.633339 | 20.82417 | 0.0156000 | * |
| 774.5404_8.982 | rel_abund_log2 | beta | control | 12 | 11 | 3.002996 | 20.75713 | 0.0068300 | ** |
| 781.62_7.043 | rel_abund_log2 | beta | control | 12 | 11 | -2.987588 | 20.62239 | 0.0071100 | ** |
| 784.5042_6.876 | rel_abund_log2 | beta | control | 12 | 11 | -2.647485 | 16.86826 | 0.0170000 | * |
| 785.6542_8.953 | rel_abund_log2 | beta | control | 12 | 11 | -2.269810 | 19.95424 | 0.0345000 | * |
| 794.6868_10.627 | rel_abund_log2 | beta | control | 12 | 11 | -2.392037 | 20.55516 | 0.0264000 | * |
| 796.5222_9.007 | rel_abund_log2 | beta | control | 12 | 11 | 2.096164 | 20.87242 | 0.0484000 | * |
| 797.5914_8.07 | rel_abund_log2 | beta | control | 12 | 11 | 2.669145 | 16.58979 | 0.0164000 | * |
| 799.6692_9.585 | rel_abund_log2 | beta | control | 12 | 11 | -2.586140 | 20.97352 | 0.0172000 | * |
| 801.686_10.409 | rel_abund_log2 | beta | control | 12 | 11 | -2.243837 | 18.26507 | 0.0375000 | * |
| 807.6349_8.948 | rel_abund_log2 | beta | control | 12 | 11 | -2.282882 | 19.84641 | 0.0336000 | * |
| 809.6525_8.065 | rel_abund_log2 | beta | control | 12 | 11 | -2.708238 | 19.13195 | 0.0139000 | * |
| 810.6564_8.03 | rel_abund_log2 | beta | control | 12 | 11 | -2.492587 | 16.63166 | 0.0236000 | * |
| 811.6703_8.961 | rel_abund_log2 | beta | control | 12 | 11 | -2.259023 | 19.99700 | 0.0352000 | * |
| 813.6854_10.226 | rel_abund_log2 | beta | control | 12 | 11 | -2.262063 | 20.46881 | 0.0347000 | * |
| 824.6527_9.167 | rel_abund_log2 | beta | control | 12 | 11 | -2.805985 | 20.76907 | 0.0107000 | * |
| 824.6537_9.596 | rel_abund_log2 | beta | control | 12 | 11 | -2.494020 | 20.48129 | 0.0213000 | * |
| 827.6999_10.502 | rel_abund_log2 | beta | control | 12 | 11 | -2.131073 | 20.19337 | 0.0456000 | * |
| 830.6471_9.551 | rel_abund_log2 | beta | control | 12 | 11 | -2.360358 | 20.73100 | 0.0281000 | * |
| 833.6507_8.971 | rel_abund_log2 | beta | control | 12 | 11 | -2.992813 | 20.97487 | 0.0069400 | ** |
| 834.7053_6.506 | rel_abund_log2 | beta | control | 12 | 11 | -2.134743 | 20.98130 | 0.0447000 | * |
| 835.6566_8.968 | rel_abund_log2 | beta | control | 12 | 11 | -2.316831 | 20.12171 | 0.0312000 | * |
| 835.6664_10.22 | rel_abund_log2 | beta | control | 12 | 11 | -2.460218 | 18.89251 | 0.0237000 | * |
| 862.6247_6.471 | rel_abund_log2 | beta | control | 12 | 11 | -2.193547 | 19.96398 | 0.0403000 | * |
| 902.7673_8.957 | rel_abund_log2 | beta | control | 12 | 11 | -2.131871 | 20.93510 | 0.0450000 | * |
| 928.7831_8.964 | rel_abund_log2 | beta | control | 12 | 11 | -2.395480 | 18.25770 | 0.0275000 | * |
| 946.718_9.767 | rel_abund_log2 | beta | control | 12 | 11 | -2.356107 | 18.21479 | 0.0299000 | * |
| 970.718_8.858 | rel_abund_log2 | beta | control | 12 | 11 | -2.205551 | 20.58688 | 0.0389000 | * |
## [1] 84
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_ctrl_beta <- inner_join(sig_beta_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_ctrl_beta$mz_rt)## [1] "118.0864_0.597" "1192.8412_6.709" "1193.3431_6.71" "1226.4736_8.96"
## [5] "1242.5051_10.22" "1242.5053_9.906" "1548.6249_6.858" "1557.1328_6.71"
## [9] "1558.1357_6.713" "1597.1201_6.716" "1618.2939_8.956" "1619.2973_8.957"
## [13] "1624.3414_10.412" "1624.8418_9.901" "1635.8345_9.901" "536.437_10.971"
## [17] "536.437_11.201" "593.4762_4.644" "692.5581_7.191" "742.5718_8.28"
## [21] "774.5404_8.982" "781.62_7.043" "785.6542_8.953" "796.5222_9.007"
## [25] "797.5914_8.07" "799.6692_9.585" "801.686_10.409" "807.6349_8.948"
## [29] "809.6525_8.065" "810.6564_8.03" "811.6703_8.961" "813.6854_10.226"
## [33] "827.6999_10.502" "833.6507_8.971" "835.6566_8.968" "835.6664_10.22"
## [37] "902.7673_8.957" "928.7831_8.964"
# run t-tests
beta_v_red_t.test <- df_for_stats %>%
filter(treatment %in% c("beta", "red"),
period == "b3") %>%
dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ treatment,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_beta_v_red_t.test <- beta_v_red_t.test %>%
filter(p < 0.05)
kable(sig_beta_v_red_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1002.658_3.079 | rel_abund_log2 | beta | red | 12 | 12 | 2.942650 | 21.85481 | 0.00756 | ** |
| 1157.3434_6.852 | rel_abund_log2 | beta | red | 12 | 12 | -2.716960 | 20.28153 | 0.01320 | * |
| 1185.344_7.069 | rel_abund_log2 | beta | red | 12 | 12 | 2.360789 | 20.80053 | 0.02810 | * |
| 1227.7565_5.834 | rel_abund_log2 | beta | red | 12 | 12 | -2.321969 | 20.41165 | 0.03070 | * |
| 1249.7383_5.838 | rel_abund_log2 | beta | red | 12 | 12 | -2.544957 | 21.27762 | 0.01870 | * |
| 1250.8236_3.081 | rel_abund_log2 | beta | red | 12 | 12 | 2.168699 | 21.49554 | 0.04150 | * |
| 1363.015_6.835 | rel_abund_log2 | beta | red | 12 | 12 | -2.655713 | 19.88652 | 0.01520 | * |
| 1511.145_6.854 | rel_abund_log2 | beta | red | 12 | 12 | -2.263250 | 18.81052 | 0.03560 | * |
| 1536.6252_6.856 | rel_abund_log2 | beta | red | 12 | 12 | -2.213067 | 19.08980 | 0.03930 | * |
| 1542.2142_6.931 | rel_abund_log2 | beta | red | 12 | 12 | -2.458452 | 18.58234 | 0.02400 | * |
| 1551.6195_6.93 | rel_abund_log2 | beta | red | 12 | 12 | -2.381095 | 20.04404 | 0.02730 | * |
| 1583.089_7.72 | rel_abund_log2 | beta | red | 12 | 12 | -2.147913 | 18.49558 | 0.04520 | * |
| 1607.1867_8.003 | rel_abund_log2 | beta | red | 12 | 12 | -2.435178 | 21.22758 | 0.02380 | * |
| 1625.2172_8.052 | rel_abund_log2 | beta | red | 12 | 12 | 2.337877 | 19.35722 | 0.03030 | * |
| 346.0093_0.919 | rel_abund_log2 | beta | red | 12 | 12 | -2.255192 | 21.92703 | 0.03440 | * |
| 508.3578_0.689 | rel_abund_log2 | beta | red | 12 | 12 | -2.411917 | 20.00919 | 0.02560 | * |
| 515.313_3.077 | rel_abund_log2 | beta | red | 12 | 12 | 3.377368 | 17.13443 | 0.00355 | ** |
| 518.322_3.062 | rel_abund_log2 | beta | red | 12 | 12 | -2.499157 | 17.98059 | 0.02240 | * |
| 536.437_10.971 | rel_abund_log2 | beta | red | 12 | 12 | -14.700188 | 21.90006 | 0.00000 | **** |
| 536.437_11.201 | rel_abund_log2 | beta | red | 12 | 12 | 8.822292 | 18.46199 | 0.00000 | **** |
| 541.3858_4.202 | rel_abund_log2 | beta | red | 12 | 12 | -2.875875 | 20.40262 | 0.00922 | ** |
| 597.4596_3.082 | rel_abund_log2 | beta | red | 12 | 12 | 2.091192 | 21.38103 | 0.04860 | * |
| 692.5581_7.191 | rel_abund_log2 | beta | red | 12 | 12 | -2.420822 | 21.98562 | 0.02420 | * |
| 699.5412_5.971 | rel_abund_log2 | beta | red | 12 | 12 | -2.368948 | 20.58067 | 0.02770 | * |
| 717.5903_7.018 | rel_abund_log2 | beta | red | 12 | 12 | -2.375782 | 20.94224 | 0.02710 | * |
| 748.6211_9.504 | rel_abund_log2 | beta | red | 12 | 12 | -3.402255 | 21.95397 | 0.00256 | ** |
| 750.52_0.687 | rel_abund_log2 | beta | red | 12 | 12 | -2.276167 | 19.67075 | 0.03420 | * |
| 759.058_6.985 | rel_abund_log2 | beta | red | 12 | 12 | -2.616382 | 21.69609 | 0.01590 | * |
| 759.2209_6.98 | rel_abund_log2 | beta | red | 12 | 12 | -2.323769 | 21.16707 | 0.03020 | * |
| 760.2926_6.982 | rel_abund_log2 | beta | red | 12 | 12 | -2.083027 | 21.99999 | 0.04910 | * |
| 760.4052_6.983 | rel_abund_log2 | beta | red | 12 | 12 | -3.496168 | 19.14834 | 0.00239 | ** |
| 760.4788_6.981 | rel_abund_log2 | beta | red | 12 | 12 | -2.194262 | 21.76911 | 0.03920 | * |
| 760.4925_6.98 | rel_abund_log2 | beta | red | 12 | 12 | -2.721056 | 20.35010 | 0.01300 | * |
| 788.3339_8.051 | rel_abund_log2 | beta | red | 12 | 12 | 2.114345 | 20.48855 | 0.04690 | * |
| 800.5558_9.058 | rel_abund_log2 | beta | red | 12 | 12 | 2.595942 | 19.14165 | 0.01770 | * |
| 805.6187_7.848 | rel_abund_log2 | beta | red | 12 | 12 | -2.086894 | 21.70686 | 0.04890 | * |
| 806.973_6.716 | rel_abund_log2 | beta | red | 12 | 12 | -2.258215 | 18.55660 | 0.03620 | * |
| 820.6364_8.966 | rel_abund_log2 | beta | red | 12 | 12 | -2.132966 | 20.55453 | 0.04520 | * |
| 824.6537_9.596 | rel_abund_log2 | beta | red | 12 | 12 | -2.863140 | 21.80253 | 0.00909 | ** |
| 825.6815_9.335 | rel_abund_log2 | beta | red | 12 | 12 | -2.158661 | 21.89567 | 0.04210 | * |
| 830.6471_9.551 | rel_abund_log2 | beta | red | 12 | 12 | -2.230766 | 21.50994 | 0.03650 | * |
| 834.5933_5.658 | rel_abund_log2 | beta | red | 12 | 12 | -2.392944 | 21.21013 | 0.02600 | * |
| 842.638_7.268 | rel_abund_log2 | beta | red | 12 | 12 | -2.246069 | 21.90116 | 0.03510 | * |
| 846.6771_7.001 | rel_abund_log2 | beta | red | 12 | 12 | 2.693786 | 20.42515 | 0.01380 | * |
| 889.7001_7.009 | rel_abund_log2 | beta | red | 12 | 12 | -2.470332 | 17.28952 | 0.02420 | * |
| 890.7034_7.007 | rel_abund_log2 | beta | red | 12 | 12 | -2.185075 | 18.79360 | 0.04180 | * |
| 939.7152_7.328 | rel_abund_log2 | beta | red | 12 | 12 | 2.129918 | 18.89856 | 0.04650 | * |
| 972.8574_11.264 | rel_abund_log2 | beta | red | 12 | 12 | -2.817938 | 21.12523 | 0.01030 | * |
## [1] 48
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_beta_red <- inner_join(sig_beta_v_red_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_beta_red$mz_rt)## [1] "1157.3434_6.852" "1363.015_6.835" "1511.145_6.854" "1536.6252_6.856"
## [5] "536.437_10.971" "536.437_11.201" "692.5581_7.191" "699.5412_5.971"
## [9] "717.5903_7.018" "805.6187_7.848" "825.6815_9.335" "834.5933_5.658"
# run t-tests
tom_v_ctrl_t.test <- df_for_stats %>%
filter(tomato_or_control %in% c("control", "tomato"),
period == "b3") %>%
dplyr::select(subject, tomato_or_control, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ tomato_or_control,
paired = FALSE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
sig_tom_v_ctrl_t.test <- tom_v_ctrl_t.test %>%
filter(p < 0.05)
kable(sig_tom_v_ctrl_t.test)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1192.8412_6.709 | rel_abund_log2 | control | tomato | 11 | 24 | 2.428666 | 27.80026 | 0.0219000 | * |
| 1193.3431_6.71 | rel_abund_log2 | control | tomato | 11 | 24 | 2.213639 | 24.43580 | 0.0364000 | * |
| 1195.8336_6.857 | rel_abund_log2 | control | tomato | 11 | 24 | 2.160327 | 23.42098 | 0.0412000 | * |
| 1197.3456_7.08 | rel_abund_log2 | control | tomato | 11 | 24 | 2.505757 | 28.07853 | 0.0183000 | * |
| 1218.5051_9.902 | rel_abund_log2 | control | tomato | 11 | 24 | 2.677839 | 27.39888 | 0.0124000 | * |
| 1226.4736_8.96 | rel_abund_log2 | control | tomato | 11 | 24 | 3.586004 | 25.57097 | 0.0013900 | ** |
| 1227.3893_7.956 | rel_abund_log2 | control | tomato | 11 | 24 | 2.518309 | 32.77798 | 0.0169000 | * |
| 1242.5051_10.22 | rel_abund_log2 | control | tomato | 11 | 24 | 2.115289 | 20.22145 | 0.0470000 | * |
| 1329.2228_12.335 | rel_abund_log2 | control | tomato | 11 | 24 | 3.212661 | 27.64360 | 0.0033300 | ** |
| 1418.1346_6.507 | rel_abund_log2 | control | tomato | 11 | 24 | 2.620702 | 29.75576 | 0.0137000 | * |
| 1429.1272_6.513 | rel_abund_log2 | control | tomato | 11 | 24 | 2.124134 | 21.59777 | 0.0454000 | * |
| 1469.1116_6.5 | rel_abund_log2 | control | tomato | 11 | 24 | -2.169593 | 31.45420 | 0.0377000 | * |
| 1535.1521_6.713 | rel_abund_log2 | control | tomato | 11 | 24 | 2.232633 | 32.18676 | 0.0327000 | * |
| 1548.6249_6.858 | rel_abund_log2 | control | tomato | 11 | 24 | 2.275686 | 29.80436 | 0.0302000 | * |
| 1552.6273_7.034 | rel_abund_log2 | control | tomato | 11 | 24 | 2.503646 | 30.66231 | 0.0178000 | * |
| 1557.1328_6.71 | rel_abund_log2 | control | tomato | 11 | 24 | 2.406573 | 26.10839 | 0.0235000 | * |
| 1577.1237_6.867 | rel_abund_log2 | control | tomato | 11 | 24 | 2.522247 | 23.02015 | 0.0190000 | * |
| 1585.1303_6.711 | rel_abund_log2 | control | tomato | 11 | 24 | 2.237797 | 31.09485 | 0.0325000 | * |
| 1591.2114_7.764 | rel_abund_log2 | control | tomato | 11 | 24 | 2.163665 | 26.26867 | 0.0398000 | * |
| 1592.2118_7.764 | rel_abund_log2 | control | tomato | 11 | 24 | 2.725596 | 29.30585 | 0.0107000 | * |
| 1595.6827_8.019 | rel_abund_log2 | control | tomato | 11 | 24 | -2.143765 | 23.47342 | 0.0426000 | * |
| 1596.1232_6.714 | rel_abund_log2 | control | tomato | 11 | 24 | 2.274823 | 31.90041 | 0.0298000 | * |
| 1596.6247_6.715 | rel_abund_log2 | control | tomato | 11 | 24 | 2.237408 | 32.65040 | 0.0322000 | * |
| 1597.3162_8.956 | rel_abund_log2 | control | tomato | 11 | 24 | 2.861105 | 24.38081 | 0.0085300 | ** |
| 1601.3486_9.899 | rel_abund_log2 | control | tomato | 11 | 24 | 2.388334 | 29.93041 | 0.0234000 | * |
| 1611.8348_9.901 | rel_abund_log2 | control | tomato | 11 | 24 | 2.206186 | 26.65447 | 0.0362000 | * |
| 1612.335_9.9 | rel_abund_log2 | control | tomato | 11 | 24 | 2.858418 | 27.76094 | 0.0079800 | ** |
| 1618.2939_8.956 | rel_abund_log2 | control | tomato | 11 | 24 | 3.147730 | 22.29351 | 0.0046200 | ** |
| 1619.2973_8.957 | rel_abund_log2 | control | tomato | 11 | 24 | 2.878082 | 25.25003 | 0.0080300 | ** |
| 162.1126_0.545 | rel_abund_log2 | control | tomato | 11 | 24 | 3.195301 | 23.73628 | 0.0039200 | ** |
| 1622.3263_9.899 | rel_abund_log2 | control | tomato | 11 | 24 | 2.295479 | 26.07570 | 0.0300000 | * |
| 1624.3414_10.412 | rel_abund_log2 | control | tomato | 11 | 24 | 2.371611 | 18.11148 | 0.0290000 | * |
| 1624.8418_9.901 | rel_abund_log2 | control | tomato | 11 | 24 | 2.159564 | 27.59775 | 0.0397000 | * |
| 1635.8345_9.901 | rel_abund_log2 | control | tomato | 11 | 24 | 2.741381 | 31.26326 | 0.0100000 | ** |
| 1636.3352_9.901 | rel_abund_log2 | control | tomato | 11 | 24 | 2.369464 | 32.89143 | 0.0238000 | * |
| 1685.4637_11.522 | rel_abund_log2 | control | tomato | 11 | 24 | -2.196385 | 18.19751 | 0.0413000 | * |
| 184.0946_0.54 | rel_abund_log2 | control | tomato | 11 | 24 | 2.309608 | 23.26080 | 0.0301000 | * |
| 353.079_1.243 | rel_abund_log2 | control | tomato | 11 | 24 | -2.114681 | 23.04164 | 0.0455000 | * |
| 369.3508_10.879 | rel_abund_log2 | control | tomato | 11 | 24 | 2.638153 | 22.64076 | 0.0148000 | * |
| 383.1533_0.931 | rel_abund_log2 | control | tomato | 11 | 24 | -2.456402 | 31.28042 | 0.0198000 | * |
| 536.437_10.971 | rel_abund_log2 | control | tomato | 11 | 24 | -5.922077 | 30.78663 | 0.0000016 | **** |
| 536.437_11.201 | rel_abund_log2 | control | tomato | 11 | 24 | -3.500544 | 26.12831 | 0.0016900 | ** |
| 568.4266_4.64 | rel_abund_log2 | control | tomato | 11 | 24 | 2.706363 | 22.15726 | 0.0128000 | * |
| 569.4277_4.639 | rel_abund_log2 | control | tomato | 11 | 24 | 3.120319 | 29.53913 | 0.0040100 | ** |
| 585.2705_0.643 | rel_abund_log2 | control | tomato | 11 | 24 | 2.467032 | 18.70598 | 0.0235000 | * |
| 593.4762_4.644 | rel_abund_log2 | control | tomato | 11 | 24 | 2.180902 | 28.00726 | 0.0377000 | * |
| 698.6194_10.879 | rel_abund_log2 | control | tomato | 11 | 24 | 2.501330 | 14.75756 | 0.0247000 | * |
| 704.3938_6.501 | rel_abund_log2 | control | tomato | 11 | 24 | 2.555392 | 26.92491 | 0.0166000 | * |
| 704.4038_6.503 | rel_abund_log2 | control | tomato | 11 | 24 | 2.294970 | 31.10600 | 0.0286000 | * |
| 705.6376_6.502 | rel_abund_log2 | control | tomato | 11 | 24 | 2.224025 | 31.82409 | 0.0334000 | * |
| 744.5538_6.51 | rel_abund_log2 | control | tomato | 11 | 24 | -2.528446 | 30.83301 | 0.0168000 | * |
| 759.1449_6.983 | rel_abund_log2 | control | tomato | 11 | 24 | -2.513737 | 22.08206 | 0.0197000 | * |
| 765.9874_3.073 | rel_abund_log2 | control | tomato | 11 | 24 | -2.670730 | 24.23921 | 0.0133000 | * |
| 774.5404_8.982 | rel_abund_log2 | control | tomato | 11 | 24 | -2.326847 | 24.14383 | 0.0287000 | * |
| 774.5634_4.81 | rel_abund_log2 | control | tomato | 11 | 24 | -2.112777 | 28.72279 | 0.0434000 | * |
| 781.62_7.043 | rel_abund_log2 | control | tomato | 11 | 24 | 2.325685 | 19.18802 | 0.0311000 | * |
| 783.431_6.876 | rel_abund_log2 | control | tomato | 11 | 24 | 2.212887 | 26.71419 | 0.0356000 | * |
| 784.5042_6.876 | rel_abund_log2 | control | tomato | 11 | 24 | 2.378216 | 30.74739 | 0.0238000 | * |
| 784.6324_6.878 | rel_abund_log2 | control | tomato | 11 | 24 | 2.438229 | 32.27817 | 0.0204000 | * |
| 785.6542_8.953 | rel_abund_log2 | control | tomato | 11 | 24 | 2.460846 | 24.57018 | 0.0213000 | * |
| 794.6868_10.627 | rel_abund_log2 | control | tomato | 11 | 24 | 2.481969 | 19.68921 | 0.0222000 | * |
| 794.7051_10.958 | rel_abund_log2 | control | tomato | 11 | 24 | 2.249156 | 32.96811 | 0.0313000 | * |
| 797.5914_8.07 | rel_abund_log2 | control | tomato | 11 | 24 | -2.758682 | 13.17839 | 0.0161000 | * |
| 799.6692_9.585 | rel_abund_log2 | control | tomato | 11 | 24 | 2.296770 | 22.88124 | 0.0311000 | * |
| 801.686_10.409 | rel_abund_log2 | control | tomato | 11 | 24 | 2.064494 | 26.70938 | 0.0488000 | * |
| 809.6525_8.065 | rel_abund_log2 | control | tomato | 11 | 24 | 2.284442 | 15.98990 | 0.0363000 | * |
| 811.6703_8.961 | rel_abund_log2 | control | tomato | 11 | 24 | 2.354898 | 24.37721 | 0.0269000 | * |
| 813.6854_10.226 | rel_abund_log2 | control | tomato | 11 | 24 | 2.420896 | 25.44892 | 0.0229000 | * |
| 813.6866_9.902 | rel_abund_log2 | control | tomato | 11 | 24 | 2.247674 | 31.67981 | 0.0317000 | * |
| 824.6527_9.167 | rel_abund_log2 | control | tomato | 11 | 24 | 2.237067 | 24.65455 | 0.0346000 | * |
| 826.594_5.988 | rel_abund_log2 | control | tomato | 11 | 24 | -2.830945 | 18.73232 | 0.0108000 | * |
| 833.6507_8.971 | rel_abund_log2 | control | tomato | 11 | 24 | 2.523234 | 20.54359 | 0.0200000 | * |
| 834.7053_6.506 | rel_abund_log2 | control | tomato | 11 | 24 | 2.286380 | 17.25444 | 0.0351000 | * |
| 835.6664_10.22 | rel_abund_log2 | control | tomato | 11 | 24 | 2.610768 | 29.56203 | 0.0140000 | * |
| 844.6529_8.109 | rel_abund_log2 | control | tomato | 11 | 24 | -2.446364 | 32.55766 | 0.0200000 | * |
| 859.6895_6.984 | rel_abund_log2 | control | tomato | 11 | 24 | -2.116553 | 30.10666 | 0.0427000 | * |
| 902.7673_8.957 | rel_abund_log2 | control | tomato | 11 | 24 | 2.357429 | 21.43887 | 0.0280000 | * |
| 928.7831_8.964 | rel_abund_log2 | control | tomato | 11 | 24 | 2.778580 | 27.08753 | 0.0098000 | ** |
## [1] 78
Keep sig features in t-test that have a match in sig ANOVA
sig_overlap_tom_ctrl <- inner_join(sig_tom_v_ctrl_t.test,
trt_tukeyHSD_sig,
by = "mz_rt",
suffix = c(".t-test", ".tukeys"))
# which features overlap?
unique(sig_overlap_tom_ctrl$mz_rt)## [1] "1192.8412_6.709" "1193.3431_6.71" "1226.4736_8.96" "1242.5051_10.22"
## [5] "1548.6249_6.858" "1557.1328_6.71" "1618.2939_8.956" "1619.2973_8.957"
## [9] "162.1126_0.545" "1624.3414_10.412" "1624.8418_9.901" "1635.8345_9.901"
## [13] "536.437_10.971" "536.437_11.201" "593.4762_4.644" "774.5404_8.982"
## [17] "781.62_7.043" "785.6542_8.953" "797.5914_8.07" "799.6692_9.585"
## [21] "801.686_10.409" "809.6525_8.065" "811.6703_8.961" "813.6854_10.226"
## [25] "813.6866_9.902" "833.6507_8.971" "835.6664_10.22" "902.7673_8.957"
## [29] "928.7831_8.964"
# go back to wide for stats df
df_for_stats_wide <- df_for_stats %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)
ANOVA_trtperiod_heatmap_data <- df_for_stats_wide %>%
filter(period == "b3") %>%
dplyr::select(sample,
all_of(trt_anova_sig$mz_rt)) %>%
column_to_rownames("sample")
head(ANOVA_trtperiod_heatmap_data, n=3)## 118.0864_0.597 1341.0272_6.83 1363.015_6.835
## x5107_b3_beta_c18pos_28 18.32788 10.56867 11.43975
## x5109_b3_beta_c18pos_81 18.08614 10.41244 12.33854
## x5112_b3_beta_c18pos_56 18.08859 11.01556 11.90621
## 1365.0287_6.836 1531.6527_7.711 1585.2008_8.971
## x5107_b3_beta_c18pos_28 12.05681 13.46108 13.04438
## x5109_b3_beta_c18pos_81 12.47198 12.59145 12.83533
## x5112_b3_beta_c18pos_56 11.93622 12.77310 12.86113
## 1586.2043_8.972 286.1437_1.13 308.1855_0.633
## x5107_b3_beta_c18pos_28 13.26054 15.59530 12.60870
## x5109_b3_beta_c18pos_81 12.85651 16.47807 13.91225
## x5112_b3_beta_c18pos_56 13.09636 15.03361 13.12646
## 312.1597_1.413 373.2483_1.147 464.1914_0.656
## x5107_b3_beta_c18pos_28 13.99762 13.78420 12.43741
## x5109_b3_beta_c18pos_81 13.61853 14.30685 13.92181
## x5112_b3_beta_c18pos_56 13.47646 13.18580 12.64051
## 526.2644_1.714 536.437_10.971 536.437_11.201
## x5107_b3_beta_c18pos_28 15.38726 14.13626 15.76905
## x5109_b3_beta_c18pos_81 15.60151 13.64178 15.56396
## x5112_b3_beta_c18pos_56 15.45049 14.33177 15.82453
## 593.4757_4.54 593.4762_4.644 593.4768_4.312
## x5107_b3_beta_c18pos_28 11.81759 12.06375 11.55068
## x5109_b3_beta_c18pos_81 11.72187 11.96809 11.87066
## x5112_b3_beta_c18pos_56 11.77517 11.61433 11.76313
## 596.4154_4.906 605.5497_9.629 610.431_5.16
## x5107_b3_beta_c18pos_28 13.59447 13.56674 14.05365
## x5109_b3_beta_c18pos_81 13.80423 12.90176 13.80027
## x5112_b3_beta_c18pos_56 13.21160 13.04551 13.89478
## 650.6449_10.83 691.5742_6.403 692.6335_11.899
## x5107_b3_beta_c18pos_28 17.57799 12.29287 15.66660
## x5109_b3_beta_c18pos_81 17.31465 12.13172 15.67945
## x5112_b3_beta_c18pos_56 17.52072 12.27323 16.44264
## 693.6368_11.899 719.6006_7.392 738.543_8.181
## x5107_b3_beta_c18pos_28 14.82319 12.66114 13.76122
## x5109_b3_beta_c18pos_81 14.81199 12.15745 12.91039
## x5112_b3_beta_c18pos_56 15.45162 12.72659 13.40845
## 743.6054_7.227 760.5883_7.719 761.3681_7.718
## x5107_b3_beta_c18pos_28 13.93291 22.20178 12.80861
## x5109_b3_beta_c18pos_81 13.72383 21.82823 11.48494
## x5112_b3_beta_c18pos_56 13.47557 21.99390 12.37175
## 761.4545_7.719 773.6516_9.025 775.6661_9.814
## x5107_b3_beta_c18pos_28 13.03432 12.89621 12.33325
## x5109_b3_beta_c18pos_81 12.09432 12.85221 12.39933
## x5112_b3_beta_c18pos_56 11.75753 13.11036 12.78094
## 781.62_7.043 784.6654_10.38 787.6674_9.202
## x5107_b3_beta_c18pos_28 12.91163 15.55655 14.21447
## x5109_b3_beta_c18pos_81 12.93878 15.10685 13.97722
## x5112_b3_beta_c18pos_56 13.25657 15.17120 14.47030
## 798.6518_8.391 807.6349_8.948 809.6525_8.065
## x5107_b3_beta_c18pos_28 14.13292 16.29715 15.81183
## x5109_b3_beta_c18pos_81 13.87908 16.11296 15.72594
## x5112_b3_beta_c18pos_56 14.14549 15.93857 15.72482
## 810.6564_8.03 813.6834_9.437 825.6815_9.335
## x5107_b3_beta_c18pos_28 15.16108 13.53442 13.16572
## x5109_b3_beta_c18pos_81 15.19539 13.41267 13.13477
## x5112_b3_beta_c18pos_56 15.23552 14.05136 13.62055
## 825.682_9.561 827.6998_10.277 827.6999_10.389
## x5107_b3_beta_c18pos_28 13.41194 16.75550 16.32508
## x5109_b3_beta_c18pos_81 13.17990 16.42048 15.16306
## x5112_b3_beta_c18pos_56 13.48333 16.99047 15.81504
## 828.702_10.497 831.6353_8.196 856.5819_7.24 859.53_8.35
## x5107_b3_beta_c18pos_28 14.89963 12.80072 12.88760 12.14271
## x5109_b3_beta_c18pos_81 14.39998 12.92834 12.93171 11.76759
## x5112_b3_beta_c18pos_56 14.89397 13.25294 12.93838 11.31550
## 865.5795_9.626 876.5695_7.88 878.7033_7.717
## x5107_b3_beta_c18pos_28 13.42176 12.81246 14.79617
## x5109_b3_beta_c18pos_81 12.52009 12.23240 14.61901
## x5112_b3_beta_c18pos_56 12.61819 12.72047 14.60653
## 899.7091_11.203 912.8727_11.58
## x5107_b3_beta_c18pos_28 14.00587 12.14743
## x5109_b3_beta_c18pos_81 14.69047 12.11961
## x5112_b3_beta_c18pos_56 15.20439 12.09212
# pull metadata
metadata_Heatmap <- metadata
# change treatment to factor
metadata_Heatmap$treatment <- as.factor(metadata_Heatmap$treatment)
# make it so that rownames in metadata match rownames from heatmap df
rownames(metadata_Heatmap) <- rownames(ANOVA_trtperiod_heatmap_data)
# create annotation rows for treatment and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_trt_row <- as.data.frame(rownames(metadata_Heatmap))
# pull trt column
anno_trt_row$treatment <- metadata_Heatmap$treatment
anno_trt_row$sex <- metadata_Heatmap$sex
# select trt
anno_trt_row <- anno_trt_row %>%
dplyr::select(treatment, sex)
# get rownames to match heatmap again
rownames(anno_trt_row) <- rownames(metadata_Heatmap)# create annotation colors
annotation_colors <- list(treatment = c("beta" = "orange",
"control" = "green",
"red" = "tomato"),
sex = c("M" = "burlywood",
"F" = "pink"))pheatmap(t(ANOVA_trtperiod_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = TRUE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+) \nShowing only post-intervention timepoints") # Without hierarchical clustering of samples (cols)
pheatmap(t(ANOVA_trtperiod_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = FALSE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in ANOVA across treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+) \nShowing only post-intervention timepoints") unpaired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(period == "b3") %>%
dplyr::select(sample,
all_of(sig_overlap_ctrl_beta$mz_rt),
all_of(sig_overlap_ctrl_red$mz_rt),
all_of(sig_overlap_tom_ctrl$mz_rt),
all_of(sig_overlap_beta_red$mz_rt)) %>%
column_to_rownames("sample")
head(unpaired_t.tests_heatmap_data,n = 3)## 118.0864_0.597 1192.8412_6.709 1193.3431_6.71
## x5107_b3_beta_c18pos_28 18.32788 12.08344 12.67894
## x5109_b3_beta_c18pos_81 18.08614 12.38855 12.49767
## x5112_b3_beta_c18pos_56 18.08859 11.98702 12.65512
## 1226.4736_8.96 1242.5051_10.22 1242.5053_9.906
## x5107_b3_beta_c18pos_28 11.69151 11.37740 12.56389
## x5109_b3_beta_c18pos_81 11.00887 10.85121 11.97213
## x5112_b3_beta_c18pos_56 11.89162 11.32887 12.63237
## 1548.6249_6.858 1557.1328_6.71 1558.1357_6.713
## x5107_b3_beta_c18pos_28 12.36216 12.16220 12.32980
## x5109_b3_beta_c18pos_81 11.95849 12.28881 12.53556
## x5112_b3_beta_c18pos_56 12.66611 11.93961 12.36858
## 1597.1201_6.716 1618.2939_8.956 1619.2973_8.957
## x5107_b3_beta_c18pos_28 12.14798 11.65769 11.70411
## x5109_b3_beta_c18pos_81 11.78014 10.91371 10.89563
## x5112_b3_beta_c18pos_56 11.98181 11.25733 11.47127
## 1624.3414_10.412 1624.8418_9.901 1635.8345_9.901
## x5107_b3_beta_c18pos_28 12.01037 12.39008 12.24433
## x5109_b3_beta_c18pos_81 11.48720 11.41456 11.10514
## x5112_b3_beta_c18pos_56 11.95295 11.91574 12.08204
## 536.437_10.971 536.437_11.201 593.4762_4.644
## x5107_b3_beta_c18pos_28 14.13626 15.76905 12.06375
## x5109_b3_beta_c18pos_81 13.64178 15.56396 11.96809
## x5112_b3_beta_c18pos_56 14.33177 15.82453 11.61433
## 692.5581_7.191 742.5718_8.28 774.5404_8.982
## x5107_b3_beta_c18pos_28 12.69678 13.67331 15.76036
## x5109_b3_beta_c18pos_81 12.95266 13.50327 15.53734
## x5112_b3_beta_c18pos_56 13.21042 13.77782 15.63822
## 781.62_7.043 785.6542_8.953 796.5222_9.007
## x5107_b3_beta_c18pos_28 12.91163 19.29489 13.00877
## x5109_b3_beta_c18pos_81 12.93878 18.54979 13.00010
## x5112_b3_beta_c18pos_56 13.25657 18.78619 12.89030
## 797.5914_8.07 799.6692_9.585 801.686_10.409
## x5107_b3_beta_c18pos_28 14.20812 17.69257 20.06030
## x5109_b3_beta_c18pos_81 14.18611 17.08662 19.54080
## x5112_b3_beta_c18pos_56 14.30850 17.43607 19.99056
## 807.6349_8.948 809.6525_8.065 810.6564_8.03
## x5107_b3_beta_c18pos_28 16.29715 15.81183 15.16108
## x5109_b3_beta_c18pos_81 16.11296 15.72594 15.19539
## x5112_b3_beta_c18pos_56 15.93857 15.72482 15.23552
## 811.6703_8.961 813.6854_10.226 827.6999_10.502
## x5107_b3_beta_c18pos_28 20.12794 19.05654 15.71804
## x5109_b3_beta_c18pos_81 19.20709 18.24428 15.20246
## x5112_b3_beta_c18pos_56 19.82178 18.58425 15.53037
## 833.6507_8.971 835.6566_8.968 835.6664_10.22
## x5107_b3_beta_c18pos_28 16.88208 14.36366 16.87449
## x5109_b3_beta_c18pos_81 16.77536 14.08959 16.26508
## x5112_b3_beta_c18pos_56 17.03756 14.35579 16.51209
## 902.7673_8.957 928.7831_8.964 1594.0976_6.869
## x5107_b3_beta_c18pos_28 12.56672 13.04733 12.09862
## x5109_b3_beta_c18pos_81 11.83950 12.61397 11.83166
## x5112_b3_beta_c18pos_56 12.12956 12.82448 11.78884
## 162.1126_0.545 813.6866_9.902 1157.3434_6.852
## x5107_b3_beta_c18pos_28 17.23696 20.65068 12.79860
## x5109_b3_beta_c18pos_81 17.10793 19.67281 12.84831
## x5112_b3_beta_c18pos_56 17.04075 20.58214 13.12909
## 1363.015_6.835 1511.145_6.854 1536.6252_6.856
## x5107_b3_beta_c18pos_28 11.43975 12.41331 12.11997
## x5109_b3_beta_c18pos_81 12.33854 11.73058 11.81181
## x5112_b3_beta_c18pos_56 11.90621 12.24789 12.34566
## 699.5412_5.971 717.5903_7.018 805.6187_7.848
## x5107_b3_beta_c18pos_28 12.15782 15.52020 13.66922
## x5109_b3_beta_c18pos_81 11.89810 15.39155 13.45917
## x5112_b3_beta_c18pos_56 12.46739 15.37404 13.84118
## 825.6815_9.335 834.5933_5.658
## x5107_b3_beta_c18pos_28 13.16572 12.83982
## x5109_b3_beta_c18pos_81 13.13477 13.01383
## x5112_b3_beta_c18pos_56 13.62055 12.85898
pheatmap(t(unpaired_t.tests_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = TRUE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+)")# without clustering of cols
pheatmap(t(unpaired_t.tests_heatmap_data),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_trt_row,
annotation_colors = annotation_colors,
cluster_cols = FALSE,
show_colnames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 3,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+)")red_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment == "red") %>%
dplyr::select(sample, period, treatment, sex,
all_of(red_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_red_row_paired <- as.data.frame(rownames(red_paired_t.tests_heatmap_data))
# pull period into a column
anno_red_row_paired$period <- red_paired_t.tests_heatmap_data$period
anno_red_row_paired$sex <- red_paired_t.tests_heatmap_data$sex
# select cols
anno_red_row_paired <- anno_red_row_paired %>%
dplyr::select(period, sex)
# get rownames to match heatmap again
rownames(anno_red_row_paired) <- rownames(red_paired_t.tests_heatmap_data)# create annotation colors
red_annotation_colors <- list(period = c("b1" = "darksalmon",
"b3" = "red"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(red_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_red_row_paired,
annotation_colors = red_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 2,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-High-Lyc paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")beta_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment == "beta") %>%
dplyr::select(sample, period, treatment, sex,
all_of(beta_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_beta_row_paired <- as.data.frame(rownames(beta_paired_t.tests_heatmap_data))
# pull period into a column
anno_beta_row_paired$period <- beta_paired_t.tests_heatmap_data$period
anno_beta_row_paired$sex <- beta_paired_t.tests_heatmap_data$sex
# select cols
anno_beta_row_paired <- anno_beta_row_paired %>%
dplyr::select(sex, period)
# get rownames to match heatmap again
rownames(anno_beta_row_paired) <- rownames(beta_paired_t.tests_heatmap_data)# create annotation colors
beta_annotation_colors <- list(period = c("b1" = "bisque",
"b3" = "darkorange"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(beta_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_beta_row_paired,
annotation_colors = beta_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 10,
cutree_cols = 5,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-High-beta paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")tom_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
filter(treatment != "control") %>%
dplyr::select(sample, period, tomato_or_control, sex,
all_of(tom_sig_ANOVA_overlap_paired$mz_rt)) %>%
mutate_at("period", as.factor) %>%
column_to_rownames("sample")# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_tom_row_paired <- as.data.frame(rownames(tom_paired_t.tests_heatmap_data))
# pull period into a column
anno_tom_row_paired$period <- tom_paired_t.tests_heatmap_data$period
anno_tom_row_paired$sex <- tom_paired_t.tests_heatmap_data$sex
# select cols
anno_tom_row_paired <- anno_tom_row_paired %>%
dplyr::select(period, sex)
# get rownames to match heatmap again
rownames(anno_tom_row_paired) <- rownames(tom_paired_t.tests_heatmap_data)# create annotation colors
tom_annotation_colors <- list(period = c("b1" = "darksalmon",
"b3" = "tomato"),
sex = c("M" = "aquamarine2",
"F" = "pink"))pheatmap(t(tom_paired_t.tests_heatmap_data[,-c(1:3)]),
scale = "row",
cluster_rows = TRUE,
annotation_col = anno_tom_row_paired,
annotation_colors = tom_annotation_colors,
cluster_cols = TRUE,
show_rownames = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
cutree_rows = 8,
cutree_cols = 2,
clustering_method = "ward.D2",
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of significant features pre- vs. post-Tomato paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")df_for_stats <- df_for_stats %>%
# add rel abund levels back since this got lost during drift correction
mutate(rel_abund = 2^(rel_abund_log2))How many features are significant (with background diet effect removed)
## [1] 460 20
Which features are significant in unpaired t test comparisons?
# select only significant features from pre v post tomato effect that have a matching key to significant features post tomato v. post control comparison
overall_tomato_effect <- semi_join(sig_paired_tom_rmBG,
sig_tom_v_ctrl_t.test,
by = "mz_rt")
dim(overall_tomato_effect)## [1] 14 20
(FC_tomato_effect <- df_for_stats %>%
filter(mz_rt %in% overall_tomato_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 14 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1195.8336_6.857 1.01 1.05 1.05 1.04
## 2 1585.1303_6.711 1.24 1.13 1.26 0.913
## 3 1596.1232_6.714 1.23 1.23 1.22 1.00
## 4 1611.8348_9.901 0.976 0.878 0.981 0.900
## 5 1619.2973_8.957 0.972 0.874 0.909 0.900
## 6 1624.8418_9.901 1.21 1.22 1.25 1.01
## 7 1635.8345_9.901 1.11 1.17 1.16 1.05
## 8 1636.3352_9.901 1.25 1.22 1.10 0.978
## 9 536.437_11.201 0.806 2.62 0.893 3.25
## 10 704.3938_6.501 1.05 0.944 0.896 0.897
## 11 744.5538_6.51 1.09 1.16 1.08 1.07
## 12 774.5404_8.982 1.14 1.20 1.17 1.05
## 13 794.6868_10.627 1.02 0.937 0.925 0.920
## 14 813.6866_9.902 1.13 1.19 1.16 1.05
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
tom_effect_list <- left_join(FC_tomato_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
tom_effect_list <-
left_join(tom_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(tom_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 1195.833… 1196. 6.86 1195.8336… 1195.8336_6.857 1 1.01
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# make comparison list for comparison tests
my_comparisons <- list( c("beta_b1", "beta_b3"),
c("red_b1", "red_b3"),
c("control_b1", "control_b3") )# subset df for uniquely significant features
justT_tom_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_tomato_effect$mz_rt)))
# make tidy df
justT_tom_effect_df_tidy <- justT_tom_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_tom_effect_df_tidy$treatment_period <- factor(justT_tom_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_tom_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_tom_effect_df_tidy$treatment <- factor(justT_tom_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))justT_tom_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y", ncol = 4) +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "Tomato effect - significant when compared to control, also significant pre and post tomato",
subtitle = "FDR adj. p-values from T-tests")## [1] 342 20
Which features are significant in paired t test comparisons for beta, but not significant in the same direction for control?
Features that are significant in both pre- vs. post. beta AND post-beta vs. post-control
# select features that are only significant post beta v. post control comparison
overall_beta_effect <- semi_join(sig_paired_beta_rmBG,
sig_beta_v_ctrl_t.test,
by = "mz_rt")
dim(overall_beta_effect)## [1] 8 20
overall_unique_beta_effect <- left_join(overall_beta_effect,
sig_beta_v_red_t.test,
by = "mz_rt")
dim(overall_unique_beta_effect)## [1] 8 29
(FC_beta_effect <- df_for_stats %>%
filter(mz_rt %in% overall_beta_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 8 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1002.658_3.079 0.988 1.10 0.971 1.11
## 2 1611.8348_9.901 0.976 0.878 0.981 0.900
## 3 1618.2939_8.956 0.997 0.859 1.01 0.862
## 4 1619.2973_8.957 0.972 0.874 0.909 0.900
## 5 536.437_11.201 0.806 2.62 0.893 3.25
## 6 774.5404_8.982 1.14 1.20 1.17 1.05
## 7 796.5222_9.007 1.12 1.16 1.14 1.04
## 8 835.6566_8.968 1.12 1.13 1.07 1.01
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
beta_effect_list <- left_join(FC_beta_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
beta_effect_list <-
left_join(beta_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(beta_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 1002.658… 1003. 3.08 1002.658_… 1002.658_3.079 1 0.988
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# subset df for uniquely significant features
justT_beta_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_beta_effect$mz_rt)))
# make tidy df
justT_beta_effect_df_tidy <- justT_beta_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_beta_effect_df_tidy$treatment_period <- factor(justT_beta_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_beta_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_beta_effect_df_tidy$treatment <- factor(justT_beta_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))
justT_beta_effect_df_tidy$period <- factor(justT_beta_effect_df_tidy$period,
levels = c("b1", "b3"))justT_beta_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y", nrow = 3) +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "High beta juice effect - significant when compared to control, also significant pre and post beta juice",
subtitle = "FDR adj. p-values from T-tests")(mz1002_bps <- justT_beta_effect_df_tidy %>%
filter(mz_rt == "1002.658_3.079") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "LPC 16:0 levels at each timepoint",
subtitle = "m/z 1002.658, RT 3.079 min") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none"))# for Gordon conference
(bC_bps <- justT_beta_effect_df_tidy %>%
filter(mz_rt == "536.437_11.201") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "\u03b2-Carotene levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none"))df_for_stats$period <- factor(df_for_stats$period,
levels = c("b1", "b3"))
df_for_stats %>%
filter(mz_rt == "496.342_3.082") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
496.342_3.082 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")I am plotting this mass because I believe it is LPC 16:0, which is what I believe m/z 1002 is an adduct for. These levels (for m/z 496) are unchanging, but when you look at the chromatograms, this mass is oversaturated and may be outside of range of linearity, possibly the reason why this mass doesn’t seem to change
Next, we’ll look at 991 which is likely the the dimer form of m/z 496
991.6743_3.082
df_for_stats %>%
filter(mz_rt == "991.6743_3.082") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
991.6743_3.082 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")df_for_stats %>%
filter(mz_rt == "498.346_3.082") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
498.346_3.082 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")## [1] 0.6446398
## [1] 0.6569136
## [1] 0.6569136
Here, I’m trying to see if m/z 835 is really the C14 isotopologue of m.z 833. Let’s look to see if trends are similar.
df_for_stats %>%
filter(mz_rt %in% c("833.6507_8.971", "835.6566_8.968")) %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = c("treatment", "mz_rt"),
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
833.6507_8.971, 835.6566_8.968 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")## [1] 0.9359787
These definitely should have been clustered!
## [1] 195 20
# select features that are only significant post red v. post control comparison
overall_red_effect <- semi_join(sig_paired_red_rmBG,
sig_red_v_ctrl_t.test,
by = "mz_rt")
dim(overall_red_effect)## [1] 2 20
# select features that are only significant post tomato v. post control comparison
overall_unique_red_effect <- left_join(overall_red_effect,
sig_beta_v_red_t.test,
by = "mz_rt")
dim(overall_unique_red_effect)## [1] 2 29
## # A tibble: 2 × 29
## mz_rt .y..red group1.red group2.red n1.red n2.red statistic.red df.red p.red
## <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 536.… rel_ab… b1 b3 12 12 -8.54 11 3.5 e-6
## 2 760.… rel_ab… b1 b3 12 12 -2.32 11 4.04e-2
## # ℹ 20 more variables: p.signif.red <chr>, .y..ctrl <chr>, group1.ctrl <chr>,
## # group2.ctrl <chr>, n1.ctrl <int>, n2.ctrl <int>, statistic.ctrl <dbl>,
## # df.ctrl <dbl>, p.ctrl <dbl>, p.signif.ctrl <chr>, sign <dbl>, .y. <chr>,
## # group1 <chr>, group2 <chr>, n1 <int>, n2 <int>, statistic <dbl>, df <dbl>,
## # p <dbl>, p.signif <chr>
(FC_red_effect <- df_for_stats %>%
filter(mz_rt %in% overall_red_effect$mz_rt) %>%
select(subject, treatment_period, mz_rt, rel_abund) %>%
group_by(mz_rt) %>%
pivot_wider(names_from = treatment_period,
values_from = rel_abund) %>%
mutate(control_FC = control_b3/control_b1,
beta_FC = beta_b3/beta_b1,
red_FC = red_b3/red_b1) %>%
summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
mean_beta_FC = mean(beta_FC, na.rm = TRUE),
mean_red_FC = mean(red_FC, na.rm = TRUE),
mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))## # A tibble: 2 × 7
## mz_rt mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 536.437_10.971 0.547 0.690 2.62 1.26
## 2 760.4925_6.98 0.980 0.994 1.05 1.01
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>
# combine cluster and FC info
red_effect_list <- left_join(FC_red_effect,
cluster_features,
by = "mz_rt") %>%
select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())# add in averages for each group + timepoint
red_effect_list <-
left_join(red_effect_list,
(df_for_stats %>%
group_by(treatment_period, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
by = "mz_rt")
head(red_effect_list, n=1)## # A tibble: 1 × 19
## mz_rt mz rt Cluster_ID Cluster_features Cluster_size mean_control_FC
## <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 536.437_… 536. 11.0 Cluster_5… 536.437_10.971;… 2 0.547
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## # mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## # MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## # control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>
# subset df for uniquely significant features
justT_red_effect_df <- df_for_stats_wide %>%
dplyr::select(c(1:21),
(all_of(overall_unique_red_effect$mz_rt)))
# make tidy df
justT_red_effect_df_tidy <- justT_red_effect_df %>%
pivot_longer(cols = 22:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# fix factor levels for time points
justT_red_effect_df_tidy$treatment_period <- factor(justT_red_effect_df_tidy$treatment_period,
levels = c("control_b1", "control_b3",
"beta_b1", "beta_b3",
"red_b1", "red_b3"))
# check
levels(justT_red_effect_df_tidy$treatment_period) ## [1] "control_b1" "control_b3" "beta_b1" "beta_b3" "red_b1"
## [6] "red_b3"
justT_red_effect_df_tidy$treatment <- factor(justT_red_effect_df_tidy$treatment,
levels = c("control", "beta", "red"))
levels(justT_red_effect_df_tidy$treatment)## [1] "control" "beta" "red"
justT_red_effect_df_tidy %>%
ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
geom_boxplot() +
scale_fill_manual(values = c("darkseagreen2", "darkgreen",
"tan", "orangered2",
"lavenderblush3", "darkred"),
labels = c("pre control", "post control",
"pre beta", "post beta",
"pre lyc", "post lyc")) +
scale_x_discrete(labels = c("", "", "", "", "", "")) +
geom_line(aes(group = subject, colour = subject), size = 0.2) +
theme_classic(base_size = 12, base_family = "sans") +
facet_wrap(vars(mz_rt), scales = "free_y") +
stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
labs(x = "",
y = "Log2 transformed relative abundance",
title = "High lyc effect - significant when compared to control, \nsignificant pre vs post red",
subtitle = "FDR adj. p-values from T-tests")(lycbps <- justT_red_effect_df_tidy %>%
filter(mz_rt == "536.437_10.971") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
Lycopene levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none"))(mz760bps <- justT_red_effect_df_tidy %>%
filter(mz_rt == "760.4925_6.98") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "PC levels at each timepoint",
subtitle = "m/z 760.4925, RT 6.98 min.") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none"))This feature seems to be getting massed by another mass (760.5281). Let’s see if the levels are comparable
df_for_stats %>%
filter(mz_rt == "760.5281_6.984") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
760.5281_6.984 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")It turns out that the feature masking my compound is a c14 isotopologue of 758.5734
df_for_stats %>%
filter(mz_rt == "758.5734_6.982") %>%
ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment",
line.color = "gray",
line.size = 1,
facet.by = "treatment",
short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
scale_fill_manual(values = c("lightgreen",
"orange",
"red"),
labels = c("Control",
"High \u03b2-Carotene",
"High Lycopene"),
name = "Treatment") +
scale_x_discrete(labels = c("b1" = "pre",
"b3" = "post")) +
geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
theme_classic(base_size = 18, base_family = "sans") +
labs(x = "",
y = "Log2 relative abundance",
title = "
758.5734_6.982 levels at each timepoint") +
stat_compare_means(method = "t.test", paired = TRUE) +
theme(legend.position = "none")